Speculative task flow execution

ABSTRACT

An example process includes: receiving a natural language input having a start time and including first and second portions respectively received from the start time to a first time and from the start time to a second time after the first time; determining an end time of the natural language input; executing, at least partially between the first time and the end time, a first task flow based on the first portion, including: obtaining a first executable object representing a first candidate action; executing, at least partially between the second time and the end time, a second task flow based on the second portion, including: obtaining a second executable object representing a second candidate action; in response to determining the end time, selecting a candidate action from a plurality of candidate actions each represented by a respective executable object; and executing the respective executable object representing the selected candidate action.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 63/113,654, entitled “SPECULATIVE TASK FLOW EXECUTION,” filed onNov. 13, 2020, the content of which is hereby incorporated by referencein its entirety.

FIELD

This relates generally to intelligent automated assistants and, morespecifically, to task execution by intelligent automated assistants.

BACKGROUND

Intelligent automated assistants (or digital assistants) can provide abeneficial interface between human users and electronic devices. Suchassistants can allow users to interact with devices or systems usingnatural language in spoken and/or text forms. For example, a user canprovide a speech input containing a user request to a digital assistantoperating on an electronic device. The digital assistant can interpretthe user's intent from the speech input and operationalize the user'sintent into tasks. The tasks can then be performed by executing one ormore services of the electronic device, and a relevant output responsiveto the user request can be returned to the user.

SUMMARY

Example methods are disclosed herein. An example method includes, at anelectronic device having one or more processors: receiving, from a userof the electronic device, a natural language input having a start time,the natural language input including: a first portion received from thestart time to a first time after the start time; and a second portionreceived from the start time to a second time after the first time;determining an end time of the natural language input; determining afirst representation of user intent based on the first portion of thenatural language input; determining a second representation of userintent based on the second portion of the natural language input;executing, at least partially between the first time and the end time, afirst task flow corresponding to the first representation of userintent, including: obtaining, based on the first representation of userintent, a first executable object representing a first candidate action,of a plurality of candidate actions, in accordance with a determinationthat the first candidate action is of a predetermined type; executing,at least partially between the second time and the end time, a secondtask flow corresponding to the second representation of user intent,including: obtaining, based on the second representation of user intent,a second executable object representing a second candidate action, ofthe plurality of candidate actions, in accordance with a determinationthat the second candidate action is of the predetermined type; inresponse to determining the end time of the natural language input,selecting a candidate action from the plurality of candidate actionseach represented by a respective executable object; and executing therespective executable object representing the selected candidate actionto perform the selected candidate action, where performing the selectedcandidate action includes providing an output to the user.

Example non-transitory computer-readable media are disclosed herein. Anexample non-transitory computer-readable storage medium stores one ormore programs. The one or more programs comprise instructions, whichwhen executed by one or more processors of an electronic device, causethe electronic device to: receive, from a user of the electronic device,a natural language input having a start time, the natural language inputincluding: a first portion received from the start time to a first timeafter the start time; and a second portion received from the start timeto a second time after the first time; determine an end time of thenatural language input; determine a first representation of user intentbased on the first portion of the natural language input; determine asecond representation of user intent based on the second portion of thenatural language input; execute, at least partially between the firsttime and the end time, a first task flow corresponding to the firstrepresentation of user intent, including: obtaining, based on the firstrepresentation of user intent, a first executable object representing afirst candidate action, of a plurality of candidate actions, inaccordance with a determination that the first candidate action is of apredetermined type; execute, at least partially between the second timeand the end time, a second task flow corresponding to the secondrepresentation of user intent, including: obtaining, based on the secondrepresentation of user intent, a second executable object representing asecond candidate action, of the plurality of candidate actions, inaccordance with a determination that the second candidate action is ofthe predetermined type; in response to determining the end time of thenatural language input, select a candidate action from the plurality ofcandidate actions each represented by a respective executable object;and execute the respective executable object representing the selectedcandidate action to perform the selected candidate action, whereperforming the selected candidate action includes providing an output tothe user.

Example electronic devices are disclosed herein. An example electronicdevice comprises one or more processors; a memory; and one or moreprograms, where the one or more programs are stored in the memory andconfigured to be executed by the one or more processors, the one or moreprograms including instructions for: receiving, from a user of theelectronic device, a natural language input having a start time, thenatural language input including: a first portion received from thestart time to a first time after the start time; and a second portionreceived from the start time to a second time after the first time;determining an end time of the natural language input; determining afirst representation of user intent based on the first portion of thenatural language input; determining a second representation of userintent based on the second portion of the natural language input;executing, at least partially between the first time and the end time, afirst task flow corresponding to the first representation of userintent, including: obtaining, based on the first representation of userintent, a first executable object representing a first candidate action,of a plurality of candidate actions, in accordance with a determinationthat the first candidate action is of a predetermined type; executing,at least partially between the second time and the end time, a secondtask flow corresponding to the second representation of user intent,including: obtaining, based on the second representation of user intent,a second executable object representing a second candidate action, ofthe plurality of candidate actions, in accordance with a determinationthat the second candidate action is of the predetermined type; inresponse to determining the end time of the natural language input,selecting a candidate action from the plurality of candidate actionseach represented by a respective executable object; and executing therespective executable object representing the selected candidate actionto perform the selected candidate action, where performing the selectedcandidate action includes providing an output to the user.

An example electronic device comprises means for: receiving, from a userof the electronic device, a natural language input having a start time,the natural language input including: a first portion received from thestart time to a first time after the start time; and a second portionreceived from the start time to a second time after the first time;determining an end time of the natural language input; determining afirst representation of user intent based on the first portion of thenatural language input; determining a second representation of userintent based on the second portion of the natural language input;executing, at least partially between the first time and the end time, afirst task flow corresponding to the first representation of userintent, including: obtaining, based on the first representation of userintent, a first executable object representing a first candidate action,of a plurality of candidate actions, in accordance with a determinationthat the first candidate action is of a predetermined type; executing,at least partially between the second time and the end time, a secondtask flow corresponding to the second representation of user intent,including: obtaining, based on the second representation of user intent,a second executable object representing a second candidate action, ofthe plurality of candidate actions, in accordance with a determinationthat the second candidate action is of the predetermined type; inresponse to determining the end time of the natural language input,selecting a candidate action from the plurality of candidate actionseach represented by a respective executable object; and executing therespective executable object representing the selected candidate actionto perform the selected candidate action, where performing the selectedcandidate action includes providing an output to the user.

Performing the operations discussed above may allow a digital assistantto more accurately and quickly respond to a user request included innatural language input. In particular, each task flow may correspond toa different interpretation of the user request, allowing a digitalassistant to evaluate different interpretations of the user request/taskflows by identifying task flows resulting in favored and disfavoredcandidate actions (e.g., actions respectively likely and unlikely tosatisfy the user request). This may promote selection and performance ofa favored candidate action for an accurate response to the user request.Further, obtaining executable objects representing respective candidateactions in accordance with determining that the candidate actions are ofthe predetermined type may allow the digital assistant to evaluate thetask flows without outputting results perceivable by a user (e.g.,displaying a result or speaking to a user). This may be desirablebecause some of the results may be incorrect, e.g., as some of thecandidate actions may correspond to incorrect and/or incompleteinterpretations of the user request.

Further, because the task flows are executed, at least partially, beforedetermining the end time of the natural language input, some processingof the natural language input occurs prior to determining the end time.This may improve the digital assistant's response time. For example, anexecutable object representing the favored candidate action may alreadybe available for execution at the end time. Accordingly, the digitalassistant may simply execute the executable object to perform thefavored candidate action at (or shortly after) the end time. After theend time, the digital assistant may not spend additional time processingthe natural language input to identity the favored candidate action. Inthis manner, the user-device interface may be more efficient andaccurate (e.g., by enabling quick and accurate responses to userrequests, by evaluating task flows without providing potentiallyincorrect responses to user requests, by reducing user inputs to correctactions performed by the digital assistant), which additionally reducespower usage and improves battery life of the device by enabling the userto use the device more quickly and efficiently.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a system and environment forimplementing a digital assistant, according to various examples.

FIG. 2A is a block diagram illustrating a portable multifunction deviceimplementing the client-side portion of a digital assistant, accordingto various examples.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling, according to various examples.

FIG. 3 illustrates a portable multifunction device implementing theclient-side portion of a digital assistant, according to variousexamples.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface, according to various examples.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on a portable multifunction device, according to variousexamples.

FIG. 5B illustrates an exemplary user interface for a multifunctiondevice with a touch-sensitive surface that is separate from the display,according to various examples.

FIG. 6A illustrates a personal electronic device, according to variousexamples.

FIG. 6B is a block diagram illustrating a personal electronic device,according to various examples.

FIG. 7A is a block diagram illustrating a digital assistant system or aserver portion thereof, according to various examples.

FIG. 7B illustrates the functions of the digital assistant shown in FIG.7A, according to various examples.

FIG. 7C illustrates a portion of an ontology, according to variousexamples.

FIG. 8 illustrates a portion of a digital assistant module, according tovarious examples.

FIG. 9 illustrates a timeline and waveform for received natural languagespeech input, according to various examples.

FIG. 10 illustrates an electronic device providing representations ofcandidate actions and corresponding candidate text, according to variousexamples.

FIG. 11 illustrates a timeline and waveform for received naturallanguage speech input, according to various examples.

FIGS. 12A-E illustrate a process for task flow execution, according tovarious examples.

FIGS. 13A-C illustrate selection and execution of task flows, accordingto various examples.

DETAILED DESCRIPTION

In the following description of examples, reference is made to theaccompanying drawings in which are shown by way of illustration specificexamples that can be practiced. It is to be understood that otherexamples can be used and structural changes can be made withoutdeparting from the scope of the various examples.

This relates generally to executing multiple task flows eachcorresponding to a different interpretation of a user request includedin a natural language input. Each task flow may be at least partiallyexecuted prior to determining an end time of the natural language input.

Although the following description uses terms “first,” “second,” etc. todescribe various elements, these elements should not be limited by theterms. These terms are only used to distinguish one element fromanother. For example, a first input could be termed a second input, and,similarly, a second input could be termed a first input, withoutdeparting from the scope of the various described examples. The firstinput and the second input are both inputs and, in some cases, areseparate and different inputs.

The terminology used in the description of the various describedexamples herein is for the purpose of describing particular examplesonly and is not intended to be limiting. As used in the description ofthe various described examples and the appended claims, the singularforms “a,” “an,” and “the” are intended to include the plural forms aswell, unless the context clearly indicates otherwise. It will also beunderstood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed items. It will be further understood that the terms“includes,” “including,” “comprises,” and/or “comprising,” when used inthis specification, specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

The term “if” may be construed to mean “when” or “upon” or “in responseto determining” or “in response to detecting,” depending on the context.Similarly, the phrase “if it is determined” or “if [a stated conditionor event] is detected” may be construed to mean “upon determining” or“in response to determining” or “upon detecting [the stated condition orevent]” or “in response to detecting [the stated condition or event],”depending on the context.

1. System and Environment

FIG. 1 illustrates a block diagram of system 100 according to variousexamples. In some examples, system 100 implements a digital assistant.The terms “digital assistant,” “virtual assistant,” “intelligentautomated assistant,” or “automatic digital assistant” refer to anyinformation processing system that interprets natural language input inspoken and/or textual form to infer user intent, and performs actionsbased on the inferred user intent. For example, to act on an inferreduser intent, the system performs one or more of the following:identifying a task flow with steps and parameters designed to accomplishthe inferred user intent, inputting specific requirements from theinferred user intent into the task flow; executing the task flow byinvoking programs, methods, services, APIs, or the like; and generatingoutput responses to the user in an audible (e.g., speech) and/or visualform.

Specifically, a digital assistant is capable of accepting a user requestat least partially in the form of a natural language command, request,statement, narrative, and/or inquiry. Typically, the user request seekseither an informational answer or performance of a task by the digitalassistant. A satisfactory response to the user request includes aprovision of the requested informational answer, a performance of therequested task, or a combination of the two. For example, a user asksthe digital assistant a question, such as “Where am I right now?” Basedon the user's current location, the digital assistant answers, “You arein Central Park near the west gate.” The user also requests theperformance of a task, for example, “Please invite my friends to mygirlfriend's birthday party next week.” In response, the digitalassistant can acknowledge the request by saying “Yes, right away,” andthen send a suitable calendar invite on behalf of the user to each ofthe user's friends listed in the user's electronic address book. Duringperformance of a requested task, the digital assistant sometimesinteracts with the user in a continuous dialogue involving multipleexchanges of information over an extended period of time. There arenumerous other ways of interacting with a digital assistant to requestinformation or performance of various tasks. In addition to providingverbal responses and taking programmed actions, the digital assistantalso provides responses in other visual or audio forms, e.g., as text,alerts, music, videos, animations, etc.

As shown in FIG. 1, in some examples, a digital assistant is implementedaccording to a client-server model. The digital assistant includesclient-side portion 102 (hereafter “DA client 102”) executed on userdevice 104 and server-side portion 106 (hereafter “DA server 106”)executed on server system 108. DA client 102 communicates with DA server106 through one or more networks 110. DA client 102 provides client-sidefunctionalities such as user-facing input and output processing andcommunication with DA server 106. DA server 106 provides server-sidefunctionalities for any number of DA clients 102 each residing on arespective user device 104.

In some examples, DA server 106 includes client-facing I/O interface112, one or more processing modules 114, data and models 116, and I/Ointerface to external services 118. The client-facing I/O interface 112facilitates the client-facing input and output processing for DA server106. One or more processing modules 114 utilize data and models 116 toprocess speech input and determine the user's intent based on naturallanguage input. Further, one or more processing modules 114 perform taskexecution based on inferred user intent. In some examples, DA server 106communicates with external services 120 through network(s) 110 for taskcompletion or information acquisition. I/O interface to externalservices 118 facilitates such communications.

User device 104 can be any suitable electronic device. In some examples,user device 104 is a portable multifunctional device (e.g., device 200,described below with reference to FIG. 2A), a multifunctional device(e.g., device 400, described below with reference to FIG. 4), or apersonal electronic device (e.g., device 600, described below withreference to FIGS. 6A-B). A portable multifunctional device is, forexample, a mobile telephone that also contains other functions, such asPDA and/or music player functions. Specific examples of portablemultifunction devices include the Apple Watch®, iPhone®, iPod Touch®,and iPad® devices from Apple Inc. of Cupertino, Calif. Other examples ofportable multifunction devices include, without limitation,earphones/headphones, speakers, and laptop or tablet computers. Further,in some examples, user device 104 is a non-portable multifunctionaldevice. In particular, user device 104 is a desktop computer, a gameconsole, a speaker, a television, or a television set-top box. In someexamples, user device 104 includes a touch-sensitive surface (e.g.,touch screen displays and/or touchpads). Further, user device 104optionally includes one or more other physical user-interface devices,such as a physical keyboard, a mouse, and/or a joystick. Variousexamples of electronic devices, such as multifunctional devices, aredescribed below in greater detail.

Examples of communication network(s) 110 include local area networks(LAN) and wide area networks (WAN), e.g., the Internet. Communicationnetwork(s) 110 is implemented using any known network protocol,including various wired or wireless protocols, such as, for example,Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for MobileCommunications (GSM), Enhanced Data GSM Environment (EDGE), codedivision multiple access (CDMA), time division multiple access (TDMA),Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or anyother suitable communication protocol.

Server system 108 is implemented on one or more standalone dataprocessing apparatus or a distributed network of computers. In someexamples, server system 108 also employs various virtual devices and/orservices of third-party service providers (e.g., third-party cloudservice providers) to provide the underlying computing resources and/orinfrastructure resources of server system 108.

In some examples, user device 104 communicates with DA server 106 viasecond user device 122. Second user device 122 is similar or identicalto user device 104. For example, second user device 122 is similar todevices 200, 400, or 600 described below with reference to FIGS. 2A, 4,and 6A-B. User device 104 is configured to communicatively couple tosecond user device 122 via a direct communication connection, such asBluetooth, NFC, BTLE, or the like, or via a wired or wireless network,such as a local Wi-Fi network. In some examples, second user device 122is configured to act as a proxy between user device 104 and DA server106. For example, DA client 102 of user device 104 is configured totransmit information (e.g., a user request received at user device 104)to DA server 106 via second user device 122. DA server 106 processes theinformation and returns relevant data (e.g., data content responsive tothe user request) to user device 104 via second user device 122.

In some examples, user device 104 is configured to communicateabbreviated requests for data to second user device 122 to reduce theamount of information transmitted from user device 104. Second userdevice 122 is configured to determine supplemental information to add tothe abbreviated request to generate a complete request to transmit to DAserver 106. This system architecture can advantageously allow userdevice 104 having limited communication capabilities and/or limitedbattery power (e.g., a watch or a similar compact electronic device) toaccess services provided by DA server 106 by using second user device122, having greater communication capabilities and/or battery power(e.g., a mobile phone, laptop computer, tablet computer, or the like),as a proxy to DA server 106. While only two user devices 104 and 122 areshown in FIG. 1, it should be appreciated that system 100, in someexamples, includes any number and type of user devices configured inthis proxy configuration to communicate with DA server system 106.

Although the digital assistant shown in FIG. 1 includes both aclient-side portion (e.g., DA client 102) and a server-side portion(e.g., DA server 106), in some examples, the functions of a digitalassistant are implemented as a standalone application installed on auser device. In addition, the divisions of functionalities between theclient and server portions of the digital assistant can vary indifferent implementations. For instance, in some examples, the DA clientis a thin-client that provides only user-facing input and outputprocessing functions, and delegates all other functionalities of thedigital assistant to a backend server.

2. Electronic Devices

Attention is now directed toward embodiments of electronic devices forimplementing the client-side portion of a digital assistant. FIG. 2A isa block diagram illustrating portable multifunction device 200 withtouch-sensitive display system 212 in accordance with some embodiments.Touch-sensitive display 212 is sometimes called a “touch screen” forconvenience and is sometimes known as or called a “touch-sensitivedisplay system.” Device 200 includes memory 202 (which optionallyincludes one or more computer-readable storage mediums), memorycontroller 222, one or more processing units (CPUs) 220, peripheralsinterface 218, RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, input/output (I/O) subsystem 206, other input controldevices 216, and external port 224. Device 200 optionally includes oneor more optical sensors 264. Device 200 optionally includes one or morecontact intensity sensors 265 for detecting intensity of contacts ondevice 200 (e.g., a touch-sensitive surface such as touch-sensitivedisplay system 212 of device 200). Device 200 optionally includes one ormore tactile output generators 267 for generating tactile outputs ondevice 200 (e.g., generating tactile outputs on a touch-sensitivesurface such as touch-sensitive display system 212 of device 200 ortouchpad 455 of device 400). These components optionally communicateover one or more communication buses or signal lines 203.

As used in the specification and claims, the term “intensity” of acontact on a touch-sensitive surface refers to the force or pressure(force per unit area) of a contact (e.g., a finger contact) on thetouch-sensitive surface, or to a substitute (proxy) for the force orpressure of a contact on the touch-sensitive surface. The intensity of acontact has a range of values that includes at least four distinctvalues and more typically includes hundreds of distinct values (e.g., atleast 256). Intensity of a contact is, optionally, determined (ormeasured) using various approaches and various sensors or combinationsof sensors. For example, one or more force sensors underneath oradjacent to the touch-sensitive surface are, optionally, used to measureforce at various points on the touch-sensitive surface. In someimplementations, force measurements from multiple force sensors arecombined (e.g., a weighted average) to determine an estimated force of acontact. Similarly, a pressure-sensitive tip of a stylus is, optionally,used to determine a pressure of the stylus on the touch-sensitivesurface. Alternatively, the size of the contact area detected on thetouch-sensitive surface and/or changes thereto, the capacitance of thetouch-sensitive surface proximate to the contact and/or changes thereto,and/or the resistance of the touch-sensitive surface proximate to thecontact and/or changes thereto are, optionally, used as a substitute forthe force or pressure of the contact on the touch-sensitive surface. Insome implementations, the substitute measurements for contact force orpressure are used directly to determine whether an intensity thresholdhas been exceeded (e.g., the intensity threshold is described in unitscorresponding to the substitute measurements). In some implementations,the substitute measurements for contact force or pressure are convertedto an estimated force or pressure, and the estimated force or pressureis used to determine whether an intensity threshold has been exceeded(e.g., the intensity threshold is a pressure threshold measured in unitsof pressure). Using the intensity of a contact as an attribute of a userinput allows for user access to additional device functionality that mayotherwise not be accessible by the user on a reduced-size device withlimited real estate for displaying affordances (e.g., on atouch-sensitive display) and/or receiving user input (e.g., via atouch-sensitive display, a touch-sensitive surface, or aphysical/mechanical control such as a knob or a button).

As used in the specification and claims, the term “tactile output”refers to physical displacement of a device relative to a previousposition of the device, physical displacement of a component (e.g., atouch-sensitive surface) of a device relative to another component(e.g., housing) of the device, or displacement of the component relativeto a center of mass of the device that will be detected by a user withthe user's sense of touch. For example, in situations where the deviceor the component of the device is in contact with a surface of a userthat is sensitive to touch (e.g., a finger, palm, or other part of auser's hand), the tactile output generated by the physical displacementwill be interpreted by the user as a tactile sensation corresponding toa perceived change in physical characteristics of the device or thecomponent of the device. For example, movement of a touch-sensitivesurface (e.g., a touch-sensitive display or trackpad) is, optionally,interpreted by the user as a “down click” or “up click” of a physicalactuator button. In some cases, a user will feel a tactile sensationsuch as an “down click” or “up click” even when there is no movement ofa physical actuator button associated with the touch-sensitive surfacethat is physically pressed (e.g., displaced) by the user's movements. Asanother example, movement of the touch-sensitive surface is, optionally,interpreted or sensed by the user as “roughness” of the touch-sensitivesurface, even when there is no change in smoothness of thetouch-sensitive surface. While such interpretations of touch by a userwill be subject to the individualized sensory perceptions of the user,there are many sensory perceptions of touch that are common to a largemajority of users. Thus, when a tactile output is described ascorresponding to a particular sensory perception of a user (e.g., an “upclick,” a “down click,” “roughness”), unless otherwise stated, thegenerated tactile output corresponds to physical displacement of thedevice or a component thereof that will generate the described sensoryperception for a typical (or average) user.

It should be appreciated that device 200 is only one example of aportable multifunction device, and that device 200 optionally has moreor fewer components than shown, optionally combines two or morecomponents, or optionally has a different configuration or arrangementof the components. The various components shown in FIG. 2A areimplemented in hardware, software, or a combination of both hardware andsoftware, including one or more signal processing and/orapplication-specific integrated circuits.

Memory 202 includes one or more computer-readable storage mediums. Thecomputer-readable storage mediums are, for example, tangible andnon-transitory. Memory 202 includes high-speed random access memory andalso includes non-volatile memory, such as one or more magnetic diskstorage devices, flash memory devices, or other non-volatile solid-statememory devices. Memory controller 222 controls access to memory 202 byother components of device 200.

In some examples, a non-transitory computer-readable storage medium ofmemory 202 is used to store instructions (e.g., for performing aspectsof processes described below) for use by or in connection with aninstruction execution system, apparatus, or device, such as acomputer-based system, processor-containing system, or other system thatcan fetch the instructions from the instruction execution system,apparatus, or device and execute the instructions. In other examples,the instructions (e.g., for performing aspects of the processesdescribed below) are stored on a non-transitory computer-readablestorage medium (not shown) of the server system 108 or are dividedbetween the non-transitory computer-readable storage medium of memory202 and the non-transitory computer-readable storage medium of serversystem 108.

Peripherals interface 218 is used to couple input and output peripheralsof the device to CPU 220 and memory 202. The one or more processors 220run or execute various software programs and/or sets of instructionsstored in memory 202 to perform various functions for device 200 and toprocess data. In some embodiments, peripherals interface 218, CPU 220,and memory controller 222 are implemented on a single chip, such as chip204. In some other embodiments, they are implemented on separate chips.

RF (radio frequency) circuitry 208 receives and sends RF signals, alsocalled electromagnetic signals. RF circuitry 208 converts electricalsignals to/from electromagnetic signals and communicates withcommunications networks and other communications devices via theelectromagnetic signals. RF circuitry 208 optionally includes well-knowncircuitry for performing these functions, including but not limited toan antenna system, an RF transceiver, one or more amplifiers, a tuner,one or more oscillators, a digital signal processor, a CODEC chipset, asubscriber identity module (SIM) card, memory, and so forth. RFcircuitry 208 optionally communicates with networks, such as theInternet, also referred to as the World Wide Web (WWW), an intranetand/or a wireless network, such as a cellular telephone network, awireless local area network (LAN) and/or a metropolitan area network(MAN), and other devices by wireless communication. The RF circuitry 208optionally includes well-known circuitry for detecting near fieldcommunication (NFC) fields, such as by a short-range communicationradio. The wireless communication optionally uses any of a plurality ofcommunications standards, protocols, and technologies, including but notlimited to Global System for Mobile Communications (GSM), Enhanced DataGSM Environment (EDGE), high-speed downlink packet access (HSDPA),high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO),HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), nearfield communication (NFC), wideband code division multiple access(W-CDMA), code division multiple access (CDMA), time division multipleaccess (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity(Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n,and/or IEEE 802.11ac), voice over Internet Protocol (VoTP), Wi-MAX, aprotocol for e mail (e.g., Internet message access protocol (IMAP)and/or post office protocol (POP)), instant messaging (e.g., extensiblemessaging and presence protocol (XMPP), Session Initiation Protocol forInstant Messaging and Presence Leveraging Extensions (SIMPLE), InstantMessaging and Presence Service (IMPS)), and/or Short Message Service(SMS), or any other suitable communication protocol, includingcommunication protocols not yet developed as of the filing date of thisdocument.

Audio circuitry 210, speaker 211, and microphone 213 provide an audiointerface between a user and device 200. Audio circuitry 210 receivesaudio data from peripherals interface 218, converts the audio data to anelectrical signal, and transmits the electrical signal to speaker 211.Speaker 211 converts the electrical signal to human-audible sound waves.Audio circuitry 210 also receives electrical signals converted bymicrophone 213 from sound waves. Audio circuitry 210 converts theelectrical signal to audio data and transmits the audio data toperipherals interface 218 for processing. Audio data are retrieved fromand/or transmitted to memory 202 and/or RF circuitry 208 by peripheralsinterface 218. In some embodiments, audio circuitry 210 also includes aheadset jack (e.g., 312, FIG. 3). The headset jack provides an interfacebetween audio circuitry 210 and removable audio input/outputperipherals, such as output-only headphones or a headset with bothoutput (e.g., a headphone for one or both ears) and input (e.g., amicrophone).

I/O subsystem 206 couples input/output peripherals on device 200, suchas touch screen 212 and other input control devices 216, to peripheralsinterface 218. I/O subsystem 206 optionally includes display controller256, optical sensor controller 258, intensity sensor controller 259,haptic feedback controller 261, and one or more input controllers 260for other input or control devices. The one or more input controllers260 receive/send electrical signals from/to other input control devices216. The other input control devices 216 optionally include physicalbuttons (e.g., push buttons, rocker buttons, etc.), dials, sliderswitches, joysticks, click wheels, and so forth. In some alternateembodiments, input controller(s) 260 are, optionally, coupled to any (ornone) of the following: a keyboard, an infrared port, a USB port, and apointer device such as a mouse. The one or more buttons (e.g., 308, FIG.3) optionally include an up/down button for volume control of speaker211 and/or microphone 213. The one or more buttons optionally include apush button (e.g., 306, FIG. 3).

A quick press of the push button disengages a lock of touch screen 212or begin a process that uses gestures on the touch screen to unlock thedevice, as described in U.S. patent application Ser. No. 11/322,549,“Unlocking a Device by Performing Gestures on an Unlock Image,” filedDec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated byreference in its entirety. A longer press of the push button (e.g., 306)turns power to device 200 on or off. The user is able to customize afunctionality of one or more of the buttons. Touch screen 212 is used toimplement virtual or soft buttons and one or more soft keyboards.

Touch-sensitive display 212 provides an input interface and an outputinterface between the device and a user. Display controller 256 receivesand/or sends electrical signals from/to touch screen 212. Touch screen212 displays visual output to the user. The visual output includesgraphics, text, icons, video, and any combination thereof (collectivelytermed “graphics”). In some embodiments, some or all of the visualoutput correspond to user-interface objects.

Touch screen 212 has a touch-sensitive surface, sensor, or set ofsensors that accepts input from the user based on haptic and/or tactilecontact. Touch screen 212 and display controller 256 (along with anyassociated modules and/or sets of instructions in memory 202) detectcontact (and any movement or breaking of the contact) on touch screen212 and convert the detected contact into interaction withuser-interface objects (e.g., one or more soft keys, icons, web pages,or images) that are displayed on touch screen 212. In an exemplaryembodiment, a point of contact between touch screen 212 and the usercorresponds to a finger of the user.

Touch screen 212 uses LCD (liquid crystal display) technology, LPD(light emitting polymer display) technology, or LED (light emittingdiode) technology, although other display technologies may be used inother embodiments. Touch screen 212 and display controller 256 detectcontact and any movement or breaking thereof using any of a plurality oftouch sensing technologies now known or later developed, including butnot limited to capacitive, resistive, infrared, and surface acousticwave technologies, as well as other proximity sensor arrays or otherelements for determining one or more points of contact with touch screen212. In an exemplary embodiment, projected mutual capacitance sensingtechnology is used, such as that found in the iPhone® and iPod Touch®from Apple Inc. of Cupertino, Calif.

A touch-sensitive display in some embodiments of touch screen 212 isanalogous to the multi-touch sensitive touchpads described in thefollowing U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No.6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932(Westerman), and/or U.S. Patent Publication 2002/0015024A1, each ofwhich is hereby incorporated by reference in its entirety. However,touch screen 212 displays visual output from device 200, whereastouch-sensitive touchpads do not provide visual output.

A touch-sensitive display in some embodiments of touch screen 212 is asdescribed in the following applications: (1) U.S. patent applicationSer. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2,2006; (2) U.S. patent application Ser. No. 10/840,862, “MultipointTouchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No.10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30,2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures ForTouch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patentapplication Ser. No. 11/038,590, “Mode-Based Graphical User InterfacesFor Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patentapplication Ser. No. 11/228,758, “Virtual Input Device Placement On ATouch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patentapplication Ser. No. 11/228,700, “Operation Of A Computer With A TouchScreen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser.No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen VirtualKeyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No.11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. Allof these applications are incorporated by reference herein in theirentirety.

Touch screen 212 has, for example, a video resolution in excess of 100dpi. In some embodiments, the touch screen has a video resolution ofapproximately 160 dpi. The user makes contact with touch screen 212using any suitable object or appendage, such as a stylus, a finger, andso forth. In some embodiments, the user interface is designed to workprimarily with finger-based contacts and gestures, which can be lessprecise than stylus-based input due to the larger area of contact of afinger on the touch screen. In some embodiments, the device translatesthe rough finger-based input into a precise pointer/cursor position orcommand for performing the actions desired by the user.

In some embodiments, in addition to the touch screen, device 200includes a touchpad (not shown) for activating or deactivatingparticular functions. In some embodiments, the touchpad is atouch-sensitive area of the device that, unlike the touch screen, doesnot display visual output. The touchpad is a touch-sensitive surfacethat is separate from touch screen 212 or an extension of thetouch-sensitive surface formed by the touch screen.

Device 200 also includes power system 262 for powering the variouscomponents. Power system 262 includes a power management system, one ormore power sources (e.g., battery, alternating current (AC)), arecharging system, a power failure detection circuit, a power converteror inverter, a power status indicator (e.g., a light-emitting diode(LED)) and any other components associated with the generation,management and distribution of power in portable devices.

Device 200 also includes one or more optical sensors 264. FIG. 2A showsan optical sensor coupled to optical sensor controller 258 in I/Osubsystem 206. Optical sensor 264 includes charge-coupled device (CCD)or complementary metal-oxide semiconductor (CMOS) phototransistors.Optical sensor 264 receives light from the environment, projectedthrough one or more lenses, and converts the light to data representingan image. In conjunction with imaging module 243 (also called a cameramodule), optical sensor 264 captures still images or video. In someembodiments, an optical sensor is located on the back of device 200,opposite touch screen display 212 on the front of the device so that thetouch screen display is used as a viewfinder for still and/or videoimage acquisition. In some embodiments, an optical sensor is located onthe front of the device so that the user's image is obtained for videoconferencing while the user views the other video conferenceparticipants on the touch screen display. In some embodiments, theposition of optical sensor 264 can be changed by the user (e.g., byrotating the lens and the sensor in the device housing) so that a singleoptical sensor 264 is used along with the touch screen display for bothvideo conferencing and still and/or video image acquisition.

Device 200 optionally also includes one or more contact intensitysensors 265. FIG. 2A shows a contact intensity sensor coupled tointensity sensor controller 259 in I/O subsystem 206. Contact intensitysensor 265 optionally includes one or more piezoresistive strain gauges,capacitive force sensors, electric force sensors, piezoelectric forcesensors, optical force sensors, capacitive touch-sensitive surfaces, orother intensity sensors (e.g., sensors used to measure the force (orpressure) of a contact on a touch-sensitive surface). Contact intensitysensor 265 receives contact intensity information (e.g., pressureinformation or a proxy for pressure information) from the environment.In some embodiments, at least one contact intensity sensor is collocatedwith, or proximate to, a touch-sensitive surface (e.g., touch-sensitivedisplay system 212). In some embodiments, at least one contact intensitysensor is located on the back of device 200, opposite touch screendisplay 212, which is located on the front of device 200.

Device 200 also includes one or more proximity sensors 266. FIG. 2Ashows proximity sensor 266 coupled to peripherals interface 218.Alternately, proximity sensor 266 is coupled to input controller 260 inI/O subsystem 206. Proximity sensor 266 is performed as described inU.S. patent application Ser. No. 11/241,839, “Proximity Detector InHandheld Device”; Ser. No. 11/240,788, “Proximity Detector In HandheldDevice”; Ser. No. 11/620,702, “Using Ambient Light Sensor To AugmentProximity Sensor Output”; Ser. No. 11/586,862, “Automated Response ToAnd Sensing Of User Activity In Portable Devices”; and Ser. No.11/638,251, “Methods And Systems For Automatic Configuration OfPeripherals,” which are hereby incorporated by reference in theirentirety. In some embodiments, the proximity sensor turns off anddisables touch screen 212 when the multifunction device is placed nearthe user's ear (e.g., when the user is making a phone call).

Device 200 optionally also includes one or more tactile outputgenerators 267. FIG. 2A shows a tactile output generator coupled tohaptic feedback controller 261 in I/O subsystem 206. Tactile outputgenerator 267 optionally includes one or more electroacoustic devicessuch as speakers or other audio components and/or electromechanicaldevices that convert energy into linear motion such as a motor,solenoid, electroactive polymer, piezoelectric actuator, electrostaticactuator, or other tactile output generating component (e.g., acomponent that converts electrical signals into tactile outputs on thedevice). Contact intensity sensor 265 receives tactile feedbackgeneration instructions from haptic feedback module 233 and generatestactile outputs on device 200 that are capable of being sensed by a userof device 200. In some embodiments, at least one tactile outputgenerator is collocated with, or proximate to, a touch-sensitive surface(e.g., touch-sensitive display system 212) and, optionally, generates atactile output by moving the touch-sensitive surface vertically (e.g.,in/out of a surface of device 200) or laterally (e.g., back and forth inthe same plane as a surface of device 200). In some embodiments, atleast one tactile output generator sensor is located on the back ofdevice 200, opposite touch screen display 212, which is located on thefront of device 200.

Device 200 also includes one or more accelerometers 268. FIG. 2A showsaccelerometer 268 coupled to peripherals interface 218. Alternately,accelerometer 268 is coupled to an input controller 260 in I/O subsystem206. Accelerometer 268 performs, for example, as described in U.S.Patent Publication No. 20050190059, “Acceleration-based Theft DetectionSystem for Portable Electronic Devices,” and U.S. Patent Publication No.20060017692, “Methods And Apparatuses For Operating A Portable DeviceBased On An Accelerometer,” both of which are incorporated by referenceherein in their entirety. In some embodiments, information is displayedon the touch screen display in a portrait view or a landscape view basedon an analysis of data received from the one or more accelerometers.Device 200 optionally includes, in addition to accelerometer(s) 268, amagnetometer (not shown) and a GPS (or GLONASS or other globalnavigation system) receiver (not shown) for obtaining informationconcerning the location and orientation (e.g., portrait or landscape) ofdevice 200.

In some embodiments, the software components stored in memory 202include operating system 226, communication module (or set ofinstructions) 228, contact/motion module (or set of instructions) 230,graphics module (or set of instructions) 232, text input module (or setof instructions) 234, Global Positioning System (GPS) module (or set ofinstructions) 235, Digital Assistant Client Module 229, and applications(or sets of instructions) 236. Further, memory 202 stores data andmodels, such as user data and models 231. Furthermore, in someembodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) stores device/globalinternal state 257, as shown in FIGS. 2A and 4. Device/global internalstate 257 includes one or more of: active application state, indicatingwhich applications, if any, are currently active; display state,indicating what applications, views or other information occupy variousregions of touch screen display 212; sensor state, including informationobtained from the device's various sensors and input control devices216; and location information concerning the device's location and/orattitude.

Operating system 226 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communication between varioushardware and software components.

Communication module 228 facilitates communication with other devicesover one or more external ports 224 and also includes various softwarecomponents for handling data received by RF circuitry 208 and/orexternal port 224. External port 224 (e.g., Universal Serial Bus (USB),FIREWIRE, etc.) is adapted for coupling directly to other devices orindirectly over a network (e.g., the Internet, wireless LAN, etc.). Insome embodiments, the external port is a multi-pin (e.g., 30-pin)connector that is the same as, or similar to and/or compatible with, the30-pin connector used on iPod® (trademark of Apple Inc.) devices.

Contact/motion module 230 optionally detects contact with touch screen212 (in conjunction with display controller 256) and othertouch-sensitive devices (e.g., a touchpad or physical click wheel).Contact/motion module 230 includes various software components forperforming various operations related to detection of contact, such asdetermining if contact has occurred (e.g., detecting a finger-downevent), determining an intensity of the contact (e.g., the force orpressure of the contact or a substitute for the force or pressure of thecontact), determining if there is movement of the contact and trackingthe movement across the touch-sensitive surface (e.g., detecting one ormore finger-dragging events), and determining if the contact has ceased(e.g., detecting a finger-up event or a break in contact).Contact/motion module 230 receives contact data from the touch-sensitivesurface. Determining movement of the point of contact, which isrepresented by a series of contact data, optionally includes determiningspeed (magnitude), velocity (magnitude and direction), and/or anacceleration (a change in magnitude and/or direction) of the point ofcontact. These operations are, optionally, applied to single contacts(e.g., one finger contacts) or to multiple simultaneous contacts (e.g.,“multitouch”/multiple finger contacts). In some embodiments,contact/motion module 230 and display controller 256 detect contact on atouchpad.

In some embodiments, contact/motion module 230 uses a set of one or moreintensity thresholds to determine whether an operation has beenperformed by a user (e.g., to determine whether a user has “clicked” onan icon). In some embodiments, at least a subset of the intensitythresholds are determined in accordance with software parameters (e.g.,the intensity thresholds are not determined by the activation thresholdsof particular physical actuators and can be adjusted without changingthe physical hardware of device 200). For example, a mouse “click”threshold of a trackpad or touch screen display can be set to any of alarge range of predefined threshold values without changing the trackpador touch screen display hardware. Additionally, in some implementations,a user of the device is provided with software settings for adjustingone or more of the set of intensity thresholds (e.g., by adjustingindividual intensity thresholds and/or by adjusting a plurality ofintensity thresholds at once with a system-level click “intensity”parameter).

Contact/motion module 230 optionally detects a gesture input by a user.Different gestures on the touch-sensitive surface have different contactpatterns (e.g., different motions, timings, and/or intensities ofdetected contacts). Thus, a gesture is, optionally, detected bydetecting a particular contact pattern. For example, detecting a fingertap gesture includes detecting a finger-down event followed by detectinga finger-up (liftoff) event at the same position (or substantially thesame position) as the finger-down event (e.g., at the position of anicon). As another example, detecting a finger swipe gesture on thetouch-sensitive surface includes detecting a finger-down event followedby detecting one or more finger-dragging events, and subsequentlyfollowed by detecting a finger-up (liftoff) event.

Graphics module 232 includes various known software components forrendering and displaying graphics on touch screen 212 or other display,including components for changing the visual impact (e.g., brightness,transparency, saturation, contrast, or other visual property) ofgraphics that are displayed. As used herein, the term “graphics”includes any object that can be displayed to a user, including, withoutlimitation, text, web pages, icons (such as user-interface objectsincluding soft keys), digital images, videos, animations, and the like.

In some embodiments, graphics module 232 stores data representinggraphics to be used. Each graphic is, optionally, assigned acorresponding code. Graphics module 232 receives, from applicationsetc., one or more codes specifying graphics to be displayed along with,if necessary, coordinate data and other graphic property data, and thengenerates screen image data to output to display controller 256.

Haptic feedback module 233 includes various software components forgenerating instructions used by tactile output generator(s) 267 toproduce tactile outputs at one or more locations on device 200 inresponse to user interactions with device 200.

Text input module 234, which is, in some examples, a component ofgraphics module 232, provides soft keyboards for entering text invarious applications (e.g., contacts 237, email 240, IM 241, browser247, and any other application that needs text input).

GPS module 235 determines the location of the device and provides thisinformation for use in various applications (e.g., to telephone 238 foruse in location-based dialing; to camera 243 as picture/video metadata;and to applications that provide location-based services such as weatherwidgets, local yellow page widgets, and map/navigation widgets).

Digital assistant client module 229 includes various client-side digitalassistant instructions to provide the client-side functionalities of thedigital assistant. For example, digital assistant client module 229 iscapable of accepting voice input (e.g., speech input), text input, touchinput, and/or gestural input through various user interfaces (e.g.,microphone 213, accelerometer(s) 268, touch-sensitive display system212, optical sensor(s) 264, other input control devices 216, etc.) ofportable multifunction device 200. Digital assistant client module 229is also capable of providing output in audio (e.g., speech output),visual, and/or tactile forms through various output interfaces (e.g.,speaker 211, touch-sensitive display system 212, tactile outputgenerator(s) 267, etc.) of portable multifunction device 200. Forexample, output is provided as voice, sound, alerts, text messages,menus, graphics, videos, animations, vibrations, and/or combinations oftwo or more of the above. During operation, digital assistant clientmodule 229 communicates with DA server 106 using RF circuitry 208.

User data and models 231 include various data associated with the user(e.g., user-specific vocabulary data, user preference data,user-specified name pronunciations, data from the user's electronicaddress book, to-do lists, shopping lists, etc.) to provide theclient-side functionalities of the digital assistant. Further, user dataand models 231 include various models (e.g., speech recognition models,statistical language models, natural language processing models,ontology, task flow models, service models, etc.) for processing userinput and determining user intent.

In some examples, digital assistant client module 229 utilizes thevarious sensors, subsystems, and peripheral devices of portablemultifunction device 200 to gather additional information from thesurrounding environment of the portable multifunction device 200 toestablish a context associated with a user, the current userinteraction, and/or the current user input. In some examples, digitalassistant client module 229 provides the contextual information or asubset thereof with the user input to DA server 106 to help infer theuser's intent. In some examples, the digital assistant also uses thecontextual information to determine how to prepare and deliver outputsto the user. Contextual information is referred to as context data.

In some examples, the contextual information that accompanies the userinput includes sensor information, e.g., lighting, ambient noise,ambient temperature, images or videos of the surrounding environment,etc. In some examples, the contextual information can also include thephysical state of the device, e.g., device orientation, device location,device temperature, power level, speed, acceleration, motion patterns,cellular signals strength, etc. In some examples, information related tothe software state of DA server 106, e.g., running processes, installedprograms, past and present network activities, background services,error logs, resources usage, etc., and of portable multifunction device200 is provided to DA server 106 as contextual information associatedwith a user input.

In some examples, the digital assistant client module 229 selectivelyprovides information (e.g., user data 231) stored on the portablemultifunction device 200 in response to requests from DA server 106. Insome examples, digital assistant client module 229 also elicitsadditional input from the user via a natural language dialogue or otheruser interfaces upon request by DA server 106. Digital assistant clientmodule 229 passes the additional input to DA server 106 to help DAserver 106 in intent deduction and/or fulfillment of the user's intentexpressed in the user request.

A more detailed description of a digital assistant is described belowwith reference to FIGS. 7A-C. It should be recognized that digitalassistant client module 229 can include any number of the sub-modules ofdigital assistant module 726 described below.

Applications 236 include the following modules (or sets ofinstructions), or a subset or superset thereof:

-   -   Contacts module 237 (sometimes called an address book or contact        list);    -   Telephone module 238;    -   Video conference module 239;    -   E-mail client module 240;    -   Instant messaging (IM) module 241;    -   Workout support module 242;    -   Camera module 243 for still and/or video images;    -   Image management module 244;    -   Video player module;    -   Music player module;    -   Browser module 247;    -   Calendar module 248;    -   Widget modules 249, which includes, in some examples, one or        more of: weather widget 249-1, stocks widget 249-2, calculator        widget 249-3, alarm clock widget 249-4, dictionary widget 249-5,        and other widgets obtained by the user, as well as user-created        widgets 249-6;    -   Widget creator module 250 for making user-created widgets 249-6;    -   Search module 251;    -   Video and music player module 252, which merges video player        module and music player module;    -   Notes module 253;    -   Map module 254; and/or    -   Online video module 255.

Examples of other applications 236 that are stored in memory 202 includeother word processing applications, other image editing applications,drawing applications, presentation applications, JAVA-enabledapplications, encryption, digital rights management, voice recognition,and voice replication.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, contacts module 237 are used to manage an address book or contactlist (e.g., stored in application internal state 292 of contacts module237 in memory 202 or memory 470), including: adding name(s) to theaddress book; deleting name(s) from the address book; associatingtelephone number(s), e-mail address(es), physical address(es) or otherinformation with a name; associating an image with a name; categorizingand sorting names; providing telephone numbers or e-mail addresses toinitiate and/or facilitate communications by telephone 238, videoconference module 239, e-mail 240, or IM 241; and so forth.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, contact/motionmodule 230, graphics module 232, and text input module 234, telephonemodule 238 are used to enter a sequence of characters corresponding to atelephone number, access one or more telephone numbers in contactsmodule 237, modify a telephone number that has been entered, dial arespective telephone number, conduct a conversation, and disconnect orhang up when the conversation is completed. As noted above, the wirelesscommunication uses any of a plurality of communications standards,protocols, and technologies.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, optical sensor264, optical sensor controller 258, contact/motion module 230, graphicsmodule 232, text input module 234, contacts module 237, and telephonemodule 238, video conference module 239 includes executable instructionsto initiate, conduct, and terminate a video conference between a userand one or more other participants in accordance with user instructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, e-mail client module 240 includes executableinstructions to create, send, receive, and manage e-mail in response touser instructions. In conjunction with image management module 244,e-mail client module 240 makes it very easy to create and send e-mailswith still or video images taken with camera module 243.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, the instant messaging module 241 includes executableinstructions to enter a sequence of characters corresponding to aninstant message, to modify previously entered characters, to transmit arespective instant message (for example, using a Short Message Service(SMS) or Multimedia Message Service (MMS) protocol for telephony-basedinstant messages or using XMPP, SIMPLE, or IMPS for Internet-basedinstant messages), to receive instant messages, and to view receivedinstant messages. In some embodiments, transmitted and/or receivedinstant messages include graphics, photos, audio files, video filesand/or other attachments as are supported in an MMS and/or an EnhancedMessaging Service (EMS). As used herein, “instant messaging” refers toboth telephony-based messages (e.g., messages sent using SMS or MMS) andInternet-based messages (e.g., messages sent using XMPP, SIMPLE, orIMPS).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, map module 254, and music playermodule, workout support module 242 includes executable instructions tocreate workouts (e.g., with time, distance, and/or calorie burninggoals); communicate with workout sensors (sports devices); receiveworkout sensor data; calibrate sensors used to monitor a workout; selectand play music for a workout; and display, store, and transmit workoutdata.

In conjunction with touch screen 212, display controller 256, opticalsensor(s) 264, optical sensor controller 258, contact/motion module 230,graphics module 232, and image management module 244, camera module 243includes executable instructions to capture still images or video(including a video stream) and store them into memory 202, modifycharacteristics of a still image or video, or delete a still image orvideo from memory 202.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, text input module 234,and camera module 243, image management module 244 includes executableinstructions to arrange, modify (e.g., edit), or otherwise manipulate,label, delete, present (e.g., in a digital slide show or album), andstore still and/or video images.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, browser module 247 includes executable instructions tobrowse the Internet in accordance with user instructions, includingsearching, linking to, receiving, and displaying web pages or portionsthereof, as well as attachments and other files linked to web pages.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, e-mail client module 240, and browser module 247,calendar module 248 includes executable instructions to create, display,modify, and store calendars and data associated with calendars (e.g.,calendar entries, to-do lists, etc.) in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, widget modules 249 aremini-applications that can be downloaded and used by a user (e.g.,weather widget 249-1, stocks widget 249-2, calculator widget 249-3,alarm clock widget 249-4, and dictionary widget 249-5) or created by theuser (e.g., user-created widget 249-6). In some embodiments, a widgetincludes an HTML (Hypertext Markup Language) file, a CSS (CascadingStyle Sheets) file, and a JavaScript file. In some embodiments, a widgetincludes an XML (Extensible Markup Language) file and a JavaScript file(e.g., Yahoo! Widgets).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, the widget creator module 250are used by a user to create widgets (e.g., turning a user-specifiedportion of a web page into a widget).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, search module 251 includes executable instructions to search fortext, music, sound, image, video, and/or other files in memory 202 thatmatch one or more search criteria (e.g., one or more user-specifiedsearch terms) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, and browser module 247, video and musicplayer module 252 includes executable instructions that allow the userto download and play back recorded music and other sound files stored inone or more file formats, such as MP3 or AAC files, and executableinstructions to display, present, or otherwise play back videos (e.g.,on touch screen 212 or on an external, connected display via externalport 224). In some embodiments, device 200 optionally includes thefunctionality of an MP3 player, such as an iPod (trademark of AppleInc.).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, notes module 253 includes executable instructions to create andmanage notes, to-do lists, and the like in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, and browser module 247, map module 254are used to receive, display, modify, and store maps and data associatedwith maps (e.g., driving directions, data on stores and other points ofinterest at or near a particular location, and other location-baseddata) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, text input module 234, e-mail clientmodule 240, and browser module 247, online video module 255 includesinstructions that allow the user to access, browse, receive (e.g., bystreaming and/or download), play back (e.g., on the touch screen or onan external, connected display via external port 224), send an e-mailwith a link to a particular online video, and otherwise manage onlinevideos in one or more file formats, such as H.264. In some embodiments,instant messaging module 241, rather than e-mail client module 240, isused to send a link to a particular online video. Additional descriptionof the online video application can be found in U.S. Provisional PatentApplication No. 60/936,562, “Portable Multifunction Device, Method, andGraphical User Interface for Playing Online Videos,” filed Jun. 20,2007, and U.S. patent application Ser. No. 11/968,067, “PortableMultifunction Device, Method, and Graphical User Interface for PlayingOnline Videos,” filed Dec. 31, 2007, the contents of which are herebyincorporated by reference in their entirety.

Each of the above-identified modules and applications corresponds to aset of executable instructions for performing one or more functionsdescribed above and the methods described in this application (e.g., thecomputer-implemented methods and other information processing methodsdescribed herein). These modules (e.g., sets of instructions) need notbe implemented as separate software programs, procedures, or modules,and thus various subsets of these modules can be combined or otherwiserearranged in various embodiments. For example, video player module canbe combined with music player module into a single module (e.g., videoand music player module 252, FIG. 2A). In some embodiments, memory 202stores a subset of the modules and data structures identified above.Furthermore, memory 202 stores additional modules and data structuresnot described above.

In some embodiments, device 200 is a device where operation of apredefined set of functions on the device is performed exclusivelythrough a touch screen and/or a touchpad. By using a touch screen and/ora touchpad as the primary input control device for operation of device200, the number of physical input control devices (such as push buttons,dials, and the like) on device 200 is reduced.

The predefined set of functions that are performed exclusively through atouch screen and/or a touchpad optionally include navigation betweenuser interfaces. In some embodiments, the touchpad, when touched by theuser, navigates device 200 to a main, home, or root menu from any userinterface that is displayed on device 200. In such embodiments, a “menubutton” is implemented using a touchpad. In some other embodiments, themenu button is a physical push button or other physical input controldevice instead of a touchpad.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling in accordance with some embodiments. In some embodiments,memory 202 (FIG. 2A) or 470 (FIG. 4) includes event sorter 270 (e.g., inoperating system 226) and a respective application 236-1 (e.g., any ofthe aforementioned applications 237-251, 255, 480-490).

Event sorter 270 receives event information and determines theapplication 236-1 and application view 291 of application 236-1 to whichto deliver the event information. Event sorter 270 includes eventmonitor 271 and event dispatcher module 274. In some embodiments,application 236-1 includes application internal state 292, whichindicates the current application view(s) displayed on touch-sensitivedisplay 212 when the application is active or executing. In someembodiments, device/global internal state 257 is used by event sorter270 to determine which application(s) is (are) currently active, andapplication internal state 292 is used by event sorter 270 to determineapplication views 291 to which to deliver event information.

In some embodiments, application internal state 292 includes additionalinformation, such as one or more of: resume information to be used whenapplication 236-1 resumes execution, user interface state informationthat indicates information being displayed or that is ready for displayby application 236-1, a state queue for enabling the user to go back toa prior state or view of application 236-1, and a redo/undo queue ofprevious actions taken by the user.

Event monitor 271 receives event information from peripherals interface218. Event information includes information about a sub-event (e.g., auser touch on touch-sensitive display 212, as part of a multi-touchgesture). Peripherals interface 218 transmits information it receivesfrom I/O subsystem 206 or a sensor, such as proximity sensor 266,accelerometer(s) 268, and/or microphone 213 (through audio circuitry210). Information that peripherals interface 218 receives from I/Osubsystem 206 includes information from touch-sensitive display 212 or atouch-sensitive surface.

In some embodiments, event monitor 271 sends requests to the peripheralsinterface 218 at predetermined intervals. In response, peripheralsinterface 218 transmits event information. In other embodiments,peripherals interface 218 transmits event information only when there isa significant event (e.g., receiving an input above a predeterminednoise threshold and/or for more than a predetermined duration).

In some embodiments, event sorter 270 also includes a hit viewdetermination module 272 and/or an active event recognizer determinationmodule 273.

Hit view determination module 272 provides software procedures fordetermining where a sub-event has taken place within one or more viewswhen touch-sensitive display 212 displays more than one view. Views aremade up of controls and other elements that a user can see on thedisplay.

Another aspect of the user interface associated with an application is aset of views, sometimes herein called application views or userinterface windows, in which information is displayed and touch-basedgestures occur. The application views (of a respective application) inwhich a touch is detected correspond to programmatic levels within aprogrammatic or view hierarchy of the application. For example, thelowest level view in which a touch is detected is called the hit view,and the set of events that are recognized as proper inputs is determinedbased, at least in part, on the hit view of the initial touch thatbegins a touch-based gesture.

Hit view determination module 272 receives information related to subevents of a touch-based gesture. When an application has multiple viewsorganized in a hierarchy, hit view determination module 272 identifies ahit view as the lowest view in the hierarchy which should handle thesub-event. In most circumstances, the hit view is the lowest level viewin which an initiating sub-event occurs (e.g., the first sub-event inthe sequence of sub-events that form an event or potential event). Oncethe hit view is identified by the hit view determination module 272, thehit view typically receives all sub-events related to the same touch orinput source for which it was identified as the hit view.

Active event recognizer determination module 273 determines which viewor views within a view hierarchy should receive a particular sequence ofsub-events. In some embodiments, active event recognizer determinationmodule 273 determines that only the hit view should receive a particularsequence of sub-events. In other embodiments, active event recognizerdetermination module 273 determines that all views that include thephysical location of a sub-event are actively involved views, andtherefore determines that all actively involved views should receive aparticular sequence of sub-events. In other embodiments, even if touchsub-events were entirely confined to the area associated with oneparticular view, views higher in the hierarchy would still remain asactively involved views.

Event dispatcher module 274 dispatches the event information to an eventrecognizer (e.g., event recognizer 280). In embodiments including activeevent recognizer determination module 273, event dispatcher module 274delivers the event information to an event recognizer determined byactive event recognizer determination module 273. In some embodiments,event dispatcher module 274 stores in an event queue the eventinformation, which is retrieved by a respective event receiver 282.

In some embodiments, operating system 226 includes event sorter 270.Alternatively, application 236-1 includes event sorter 270. In yet otherembodiments, event sorter 270 is a stand-alone module, or a part ofanother module stored in memory 202, such as contact/motion module 230.

In some embodiments, application 236-1 includes a plurality of eventhandlers 290 and one or more application views 291, each of whichincludes instructions for handling touch events that occur within arespective view of the application's user interface. Each applicationview 291 of the application 236-1 includes one or more event recognizers280. Typically, a respective application view 291 includes a pluralityof event recognizers 280. In other embodiments, one or more of eventrecognizers 280 are part of a separate module, such as a user interfacekit (not shown) or a higher level object from which application 236-1inherits methods and other properties. In some embodiments, a respectiveevent handler 290 includes one or more of: data updater 276, objectupdater 277, GUI updater 278, and/or event data 279 received from eventsorter 270. Event handler 290 utilizes or calls data updater 276, objectupdater 277, or GUI updater 278 to update the application internal state292. Alternatively, one or more of the application views 291 include oneor more respective event handlers 290. Also, in some embodiments, one ormore of data updater 276, object updater 277, and GUI updater 278 areincluded in a respective application view 291.

A respective event recognizer 280 receives event information (e.g.,event data 279) from event sorter 270 and identifies an event from theevent information. Event recognizer 280 includes event receiver 282 andevent comparator 284. In some embodiments, event recognizer 280 alsoincludes at least a subset of: metadata 283, and event deliveryinstructions 288 (which include sub-event delivery instructions).

Event receiver 282 receives event information from event sorter 270. Theevent information includes information about a sub-event, for example, atouch or a touch movement. Depending on the sub-event, the eventinformation also includes additional information, such as location ofthe sub-event. When the sub-event concerns motion of a touch, the eventinformation also includes speed and direction of the sub-event. In someembodiments, events include rotation of the device from one orientationto another (e.g., from a portrait orientation to a landscapeorientation, or vice versa), and the event information includescorresponding information about the current orientation (also calleddevice attitude) of the device.

Event comparator 284 compares the event information to predefined eventor sub-event definitions and, based on the comparison, determines anevent or sub event, or determines or updates the state of an event orsub-event. In some embodiments, event comparator 284 includes eventdefinitions 286. Event definitions 286 contain definitions of events(e.g., predefined sequences of sub-events), for example, event 1(287-1), event 2 (287-2), and others. In some embodiments, sub-events inan event (287) include, for example, touch begin, touch end, touchmovement, touch cancellation, and multiple touching. In one example, thedefinition for event 1 (287-1) is a double tap on a displayed object.The double tap, for example, comprises a first touch (touch begin) onthe displayed object for a predetermined phase, a first liftoff (touchend) for a predetermined phase, a second touch (touch begin) on thedisplayed object for a predetermined phase, and a second liftoff (touchend) for a predetermined phase. In another example, the definition forevent 2 (287-2) is a dragging on a displayed object. The dragging, forexample, comprises a touch (or contact) on the displayed object for apredetermined phase, a movement of the touch across touch-sensitivedisplay 212, and liftoff of the touch (touch end). In some embodiments,the event also includes information for one or more associated eventhandlers 290.

In some embodiments, event definition 287 includes a definition of anevent for a respective user-interface object. In some embodiments, eventcomparator 284 performs a hit test to determine which user-interfaceobject is associated with a sub-event. For example, in an applicationview in which three user-interface objects are displayed ontouch-sensitive display 212, when a touch is detected on touch-sensitivedisplay 212, event comparator 284 performs a hit test to determine whichof the three user-interface objects is associated with the touch(sub-event). If each displayed object is associated with a respectiveevent handler 290, the event comparator uses the result of the hit testto determine which event handler 290 should be activated. For example,event comparator 284 selects an event handler associated with thesub-event and the object triggering the hit test.

In some embodiments, the definition for a respective event (287) alsoincludes delayed actions that delay delivery of the event informationuntil after it has been determined whether the sequence of sub-eventsdoes or does not correspond to the event recognizer's event type.

When a respective event recognizer 280 determines that the series ofsub-events do not match any of the events in event definitions 286, therespective event recognizer 280 enters an event impossible, eventfailed, or event ended state, after which it disregards subsequentsub-events of the touch-based gesture. In this situation, other eventrecognizers, if any, that remain active for the hit view continue totrack and process sub-events of an ongoing touch-based gesture.

In some embodiments, a respective event recognizer 280 includes metadata283 with configurable properties, flags, and/or lists that indicate howthe event delivery system should perform sub-event delivery to activelyinvolved event recognizers. In some embodiments, metadata 283 includesconfigurable properties, flags, and/or lists that indicate how eventrecognizers interact, or are enabled to interact, with one another. Insome embodiments, metadata 283 includes configurable properties, flags,and/or lists that indicate whether sub-events are delivered to varyinglevels in the view or programmatic hierarchy.

In some embodiments, a respective event recognizer 280 activates eventhandler 290 associated with an event when one or more particularsub-events of an event are recognized. In some embodiments, a respectiveevent recognizer 280 delivers event information associated with theevent to event handler 290. Activating an event handler 290 is distinctfrom sending (and deferred sending) sub-events to a respective hit view.In some embodiments, event recognizer 280 throws a flag associated withthe recognized event, and event handler 290 associated with the flagcatches the flag and performs a predefined process.

In some embodiments, event delivery instructions 288 include sub-eventdelivery instructions that deliver event information about a sub-eventwithout activating an event handler. Instead, the sub-event deliveryinstructions deliver event information to event handlers associated withthe series of sub-events or to actively involved views. Event handlersassociated with the series of sub-events or with actively involved viewsreceive the event information and perform a predetermined process.

In some embodiments, data updater 276 creates and updates data used inapplication 236-1. For example, data updater 276 updates the telephonenumber used in contacts module 237, or stores a video file used in videoplayer module. In some embodiments, object updater 277 creates andupdates objects used in application 236-1. For example, object updater277 creates a new user-interface object or updates the position of auser-interface object. GUI updater 278 updates the GUI. For example, GUIupdater 278 prepares display information and sends it to graphics module232 for display on a touch-sensitive display.

In some embodiments, event handler(s) 290 includes or has access to dataupdater 276, object updater 277, and GUI updater 278. In someembodiments, data updater 276, object updater 277, and GUI updater 278are included in a single module of a respective application 236-1 orapplication view 291. In other embodiments, they are included in two ormore software modules.

It shall be understood that the foregoing discussion regarding eventhandling of user touches on touch-sensitive displays also applies toother forms of user inputs to operate multifunction devices 200 withinput devices, not all of which are initiated on touch screens. Forexample, mouse movement and mouse button presses, optionally coordinatedwith single or multiple keyboard presses or holds; contact movementssuch as taps, drags, scrolls, etc. on touchpads; pen stylus inputs;movement of the device; oral instructions; detected eye movements;biometric inputs; and/or any combination thereof are optionally utilizedas inputs corresponding to sub-events which define an event to berecognized.

FIG. 3 illustrates a portable multifunction device 200 having a touchscreen 212 in accordance with some embodiments. The touch screenoptionally displays one or more graphics within user interface (UI) 300.In this embodiment, as well as others described below, a user is enabledto select one or more of the graphics by making a gesture on thegraphics, for example, with one or more fingers 302 (not drawn to scalein the figure) or one or more styluses 303 (not drawn to scale in thefigure). In some embodiments, selection of one or more graphics occurswhen the user breaks contact with the one or more graphics. In someembodiments, the gesture optionally includes one or more taps, one ormore swipes (from left to right, right to left, upward and/or downward),and/or a rolling of a finger (from right to left, left to right, upwardand/or downward) that has made contact with device 200. In someimplementations or circumstances, inadvertent contact with a graphicdoes not select the graphic. For example, a swipe gesture that sweepsover an application icon optionally does not select the correspondingapplication when the gesture corresponding to selection is a tap.

Device 200 also includes one or more physical buttons, such as “home” ormenu button 304. As described previously, menu button 304 is used tonavigate to any application 236 in a set of applications that isexecuted on device 200. Alternatively, in some embodiments, the menubutton is implemented as a soft key in a GUI displayed on touch screen212.

In one embodiment, device 200 includes touch screen 212, menu button304, push button 306 for powering the device on/off and locking thedevice, volume adjustment button(s) 308, subscriber identity module(SIM) card slot 310, headset jack 312, and docking/charging externalport 224. Push button 306 is, optionally, used to turn the power on/offon the device by depressing the button and holding the button in thedepressed state for a predefined time interval; to lock the device bydepressing the button and releasing the button before the predefinedtime interval has elapsed; and/or to unlock the device or initiate anunlock process. In an alternative embodiment, device 200 also acceptsverbal input for activation or deactivation of some functions throughmicrophone 213. Device 200 also, optionally, includes one or morecontact intensity sensors 265 for detecting intensity of contacts ontouch screen 212 and/or one or more tactile output generators 267 forgenerating tactile outputs for a user of device 200.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface in accordance with someembodiments. Device 400 need not be portable. In some embodiments,device 400 is a laptop computer, a desktop computer, a tablet computer,a multimedia player device, a navigation device, an educational device(such as a child's learning toy), a gaming system, or a control device(e.g., a home or industrial controller). Device 400 typically includesone or more processing units (CPUs) 410, one or more network or othercommunications interfaces 460, memory 470, and one or more communicationbuses 420 for interconnecting these components. Communication buses 420optionally include circuitry (sometimes called a chipset) thatinterconnects and controls communications between system components.Device 400 includes input/output (I/O) interface 430 comprising display440, which is typically a touch screen display. I/O interface 430 alsooptionally includes a keyboard and/or mouse (or other pointing device)450 and touchpad 455, tactile output generator 457 for generatingtactile outputs on device 400 (e.g., similar to tactile outputgenerator(s) 267 described above with reference to FIG. 2A), sensors 459(e.g., optical, acceleration, proximity, touch-sensitive, and/or contactintensity sensors similar to contact intensity sensor(s) 265 describedabove with reference to FIG. 2A). Memory 470 includes high-speed randomaccess memory, such as DRAM, SRAM, DDR RAM, or other random access solidstate memory devices; and optionally includes non-volatile memory, suchas one or more magnetic disk storage devices, optical disk storagedevices, flash memory devices, or other non-volatile solid state storagedevices. Memory 470 optionally includes one or more storage devicesremotely located from CPU(s) 410. In some embodiments, memory 470 storesprograms, modules, and data structures analogous to the programs,modules, and data structures stored in memory 202 of portablemultifunction device 200 (FIG. 2A), or a subset thereof. Furthermore,memory 470 optionally stores additional programs, modules, and datastructures not present in memory 202 of portable multifunction device200. For example, memory 470 of device 400 optionally stores drawingmodule 480, presentation module 482, word processing module 484, websitecreation module 486, disk authoring module 488, and/or spreadsheetmodule 490, while memory 202 of portable multifunction device 200 (FIG.2A) optionally does not store these modules.

Each of the above-identified elements in FIG. 4 is, in some examples,stored in one or more of the previously mentioned memory devices. Eachof the above-identified modules corresponds to a set of instructions forperforming a function described above. The above-identified modules orprograms (e.g., sets of instructions) need not be implemented asseparate software programs, procedures, or modules, and thus varioussubsets of these modules are combined or otherwise rearranged in variousembodiments. In some embodiments, memory 470 stores a subset of themodules and data structures identified above. Furthermore, memory 470stores additional modules and data structures not described above.

Attention is now directed towards embodiments of user interfaces thatcan be implemented on, for example, portable multifunction device 200.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on portable multifunction device 200 in accordance withsome embodiments. Similar user interfaces are implemented on device 400.In some embodiments, user interface 500 includes the following elements,or a subset or superset thereof:

Signal strength indicator(s) 502 for wireless communication(s), such ascellular and Wi-Fi signals;

-   -   Time 504;    -   Bluetooth indicator 505;    -   Battery status indicator 506;    -   Tray 508 with icons for frequently used applications, such as:        -   Icon 516 for telephone module 238, labeled “Phone,” which            optionally includes an indicator 514 of the number of missed            calls or voicemail messages;        -   Icon 518 for e-mail client module 240, labeled “Mail,” which            optionally includes an indicator 510 of the number of unread            e-mails;        -   Icon 520 for browser module 247, labeled “Browser;” and        -   Icon 522 for video and music player module 252, also            referred to as iPod (trademark of Apple Inc.) module 252,            labeled “iPod;” and    -   Icons for other applications, such as:        -   Icon 524 for IM module 241, labeled “Messages;”        -   Icon 526 for calendar module 248, labeled “Calendar;”        -   Icon 528 for image management module 244, labeled “Photos;”        -   Icon 530 for camera module 243, labeled “Camera;”        -   Icon 532 for online video module 255, labeled “Online            Video;”        -   Icon 534 for stocks widget 249-2, labeled “Stocks;”        -   Icon 536 for map module 254, labeled “Maps;”        -   Icon 538 for weather widget 249-1, labeled “Weather;”        -   Icon 540 for alarm clock widget 249-4, labeled “Clock;”        -   Icon 542 for workout support module 242, labeled “Workout            Support;”        -   Icon 544 for notes module 253, labeled “Notes;” and        -   Icon 546 for a settings application or module, labeled            “Settings,” which provides access to settings for device 200            and its various applications 236.

It should be noted that the icon labels illustrated in FIG. 5A aremerely exemplary. For example, icon 522 for video and music playermodule 252 is optionally labeled “Music” or “Music Player.” Other labelsare, optionally, used for various application icons. In someembodiments, a label for a respective application icon includes a nameof an application corresponding to the respective application icon. Insome embodiments, a label for a particular application icon is distinctfrom a name of an application corresponding to the particularapplication icon.

FIG. 5B illustrates an exemplary user interface on a device (e.g.,device 400, FIG. 4) with a touch-sensitive surface 551 (e.g., a tabletor touchpad 455, FIG. 4) that is separate from the display 550 (e.g.,touch screen display 212). Device 400 also, optionally, includes one ormore contact intensity sensors (e.g., one or more of sensors 457) fordetecting intensity of contacts on touch-sensitive surface 551 and/orone or more tactile output generators 459 for generating tactile outputsfor a user of device 400.

Although some of the examples which follow will be given with referenceto inputs on touch screen display 212 (where the touch-sensitive surfaceand the display are combined), in some embodiments, the device detectsinputs on a touch-sensitive surface that is separate from the display,as shown in FIG. 5B. In some embodiments, the touch-sensitive surface(e.g., 551 in FIG. 5B) has a primary axis (e.g., 552 in FIG. 5B) thatcorresponds to a primary axis (e.g., 553 in FIG. 5B) on the display(e.g., 550). In accordance with these embodiments, the device detectscontacts (e.g., 560 and 562 in FIG. 5B) with the touch-sensitive surface551 at locations that correspond to respective locations on the display(e.g., in FIG. 5B, 560 corresponds to 568 and 562 corresponds to 570).In this way, user inputs (e.g., contacts 560 and 562, and movementsthereof) detected by the device on the touch-sensitive surface (e.g.,551 in FIG. 5B) are used by the device to manipulate the user interfaceon the display (e.g., 550 in FIG. 5B) of the multifunction device whenthe touch-sensitive surface is separate from the display. It should beunderstood that similar methods are, optionally, used for other userinterfaces described herein.

Additionally, while the following examples are given primarily withreference to finger inputs (e.g., finger contacts, finger tap gestures,finger swipe gestures), it should be understood that, in someembodiments, one or more of the finger inputs are replaced with inputfrom another input device (e.g., a mouse-based input or stylus input).For example, a swipe gesture is, optionally, replaced with a mouse click(e.g., instead of a contact) followed by movement of the cursor alongthe path of the swipe (e.g., instead of movement of the contact). Asanother example, a tap gesture is, optionally, replaced with a mouseclick while the cursor is located over the location of the tap gesture(e.g., instead of detection of the contact followed by ceasing to detectthe contact). Similarly, when multiple user inputs are simultaneouslydetected, it should be understood that multiple computer mice are,optionally, used simultaneously, or a mouse and finger contacts are,optionally, used simultaneously.

FIG. 6A illustrates exemplary personal electronic device 600. Device 600includes body 602. In some embodiments, device 600 includes some or allof the features described with respect to devices 200 and 400 (e.g.,FIGS. 2A-4). In some embodiments, device 600 has touch-sensitive displayscreen 604, hereafter touch screen 604. Alternatively, or in addition totouch screen 604, device 600 has a display and a touch-sensitivesurface. As with devices 200 and 400, in some embodiments, touch screen604 (or the touch-sensitive surface) has one or more intensity sensorsfor detecting intensity of contacts (e.g., touches) being applied. Theone or more intensity sensors of touch screen 604 (or thetouch-sensitive surface) provide output data that represents theintensity of touches. The user interface of device 600 responds totouches based on their intensity, meaning that touches of differentintensities can invoke different user interface operations on device600.

Techniques for detecting and processing touch intensity are found, forexample, in related applications: International Patent ApplicationSerial No. PCT/US2013/040061, titled “Device, Method, and Graphical UserInterface for Displaying User Interface Objects Corresponding to anApplication,” filed May 8, 2013, and International Patent ApplicationSerial No. PCT/US2013/069483, titled “Device, Method, and Graphical UserInterface for Transitioning Between Touch Input to Display OutputRelationships,” filed Nov. 11, 2013, each of which is herebyincorporated by reference in their entirety.

In some embodiments, device 600 has one or more input mechanisms 606 and608. Input mechanisms 606 and 608, if included, are physical. Examplesof physical input mechanisms include push buttons and rotatablemechanisms. In some embodiments, device 600 has one or more attachmentmechanisms. Such attachment mechanisms, if included, can permitattachment of device 600 with, for example, hats, eyewear, earrings,necklaces, shirts, jackets, bracelets, watch straps, chains, trousers,belts, shoes, purses, backpacks, and so forth. These attachmentmechanisms permit device 600 to be worn by a user.

FIG. 6B depicts exemplary personal electronic device 600. In someembodiments, device 600 includes some or all of the components describedwith respect to FIGS. 2A, 2B, and 4. Device 600 has bus 612 thatoperatively couples I/O section 614 with one or more computer processors616 and memory 618. I/O section 614 is connected to display 604, whichcan have touch-sensitive component 622 and, optionally, touch-intensitysensitive component 624. In addition, I/O section 614 is connected withcommunication unit 630 for receiving application and operating systemdata, using Wi-Fi, Bluetooth, near field communication (NFC), cellular,and/or other wireless communication techniques. Device 600 includesinput mechanisms 606 and/or 608. Input mechanism 606 is a rotatableinput device or a depressible and rotatable input device, for example.Input mechanism 608 is a button, in some examples.

Input mechanism 608 is a microphone, in some examples. Personalelectronic device 600 includes, for example, various sensors, such asGPS sensor 632, accelerometer 634, directional sensor 640 (e.g.,compass), gyroscope 636, motion sensor 638, and/or a combinationthereof, all of which are operatively connected to I/O section 614.

Memory 618 of personal electronic device 600 is a non-transitorycomputer-readable storage medium, for storing computer-executableinstructions, which, when executed by one or more computer processors616, for example, cause the computer processors to perform thetechniques and processes described below. The computer-executableinstructions, for example, are also stored and/or transported within anynon-transitory computer-readable storage medium for use by or inconnection with an instruction execution system, apparatus, or device,such as a computer-based system, processor-containing system, or othersystem that can fetch the instructions from the instruction executionsystem, apparatus, or device and execute the instructions. Personalelectronic device 600 is not limited to the components and configurationof FIG. 6B, but can include other or additional components in multipleconfigurations.

As used here, the term “affordance” refers to a user-interactivegraphical user interface object that is, for example, displayed on thedisplay screen of devices 200, 400, and/or 600 (FIGS. 2A, 4, 6A-B, and10). For example, an image (e.g., icon), a button, and text (e.g.,hyperlink) each constitutes an affordance.

As used herein, the term “focus selector” refers to an input elementthat indicates a current part of a user interface with which a user isinteracting. In some implementations that include a cursor or otherlocation marker, the cursor acts as a “focus selector” so that when aninput (e.g., a press input) is detected on a touch-sensitive surface(e.g., touchpad 455 in FIG. 4 or touch-sensitive surface 551 in FIG. 5B)while the cursor is over a particular user interface element (e.g., abutton, window, slider or other user interface element), the particularuser interface element is adjusted in accordance with the detectedinput. In some implementations that include a touch screen display(e.g., touch-sensitive display system 212 in FIG. 2A or touch screen 212in FIG. 5A) that enables direct interaction with user interface elementson the touch screen display, a detected contact on the touch screen actsas a “focus selector” so that when an input (e.g., a press input by thecontact) is detected on the touch screen display at a location of aparticular user interface element (e.g., a button, window, slider, orother user interface element), the particular user interface element isadjusted in accordance with the detected input. In some implementations,focus is moved from one region of a user interface to another region ofthe user interface without corresponding movement of a cursor ormovement of a contact on a touch screen display (e.g., by using a tabkey or arrow keys to move focus from one button to another button); inthese implementations, the focus selector moves in accordance withmovement of focus between different regions of the user interface.Without regard to the specific form taken by the focus selector, thefocus selector is generally the user interface element (or contact on atouch screen display) that is controlled by the user so as tocommunicate the user's intended interaction with the user interface(e.g., by indicating, to the device, the element of the user interfacewith which the user is intending to interact). For example, the locationof a focus selector (e.g., a cursor, a contact, or a selection box) overa respective button while a press input is detected on thetouch-sensitive surface (e.g., a touchpad or touch screen) will indicatethat the user is intending to activate the respective button (as opposedto other user interface elements shown on a display of the device).

As used in the specification and claims, the term “characteristicintensity” of a contact refers to a characteristic of the contact basedon one or more intensities of the contact. In some embodiments, thecharacteristic intensity is based on multiple intensity samples. Thecharacteristic intensity is, optionally, based on a predefined number ofintensity samples, or a set of intensity samples collected during apredetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10seconds) relative to a predefined event (e.g., after detecting thecontact, prior to detecting liftoff of the contact, before or afterdetecting a start of movement of the contact, prior to detecting an endof the contact, before or after detecting an increase in intensity ofthe contact, and/or before or after detecting a decrease in intensity ofthe contact). A characteristic intensity of a contact is, optionallybased on one or more of: a maximum value of the intensities of thecontact, a mean value of the intensities of the contact, an averagevalue of the intensities of the contact, a top 10 percentile value ofthe intensities of the contact, a value at the half maximum of theintensities of the contact, a value at the 90 percent maximum of theintensities of the contact, or the like. In some embodiments, theduration of the contact is used in determining the characteristicintensity (e.g., when the characteristic intensity is an average of theintensity of the contact over time). In some embodiments, thecharacteristic intensity is compared to a set of one or more intensitythresholds to determine whether an operation has been performed by auser. For example, the set of one or more intensity thresholds includesa first intensity threshold and a second intensity threshold. In thisexample, a contact with a characteristic intensity that does not exceedthe first threshold results in a first operation, a contact with acharacteristic intensity that exceeds the first intensity threshold anddoes not exceed the second intensity threshold results in a secondoperation, and a contact with a characteristic intensity that exceedsthe second threshold results in a third operation. In some embodiments,a comparison between the characteristic intensity and one or morethresholds is used to determine whether or not to perform one or moreoperations (e.g., whether to perform a respective operation or forgoperforming the respective operation) rather than being used to determinewhether to perform a first operation or a second operation.

In some embodiments, a portion of a gesture is identified for purposesof determining a characteristic intensity. For example, atouch-sensitive surface receives a continuous swipe contacttransitioning from a start location and reaching an end location, atwhich point the intensity of the contact increases. In this example, thecharacteristic intensity of the contact at the end location is based ononly a portion of the continuous swipe contact, and not the entire swipecontact (e.g., only the portion of the swipe contact at the endlocation). In some embodiments, a smoothing algorithm is applied to theintensities of the swipe contact prior to determining the characteristicintensity of the contact. For example, the smoothing algorithmoptionally includes one or more of: an unweighted sliding-averagesmoothing algorithm, a triangular smoothing algorithm, a median filtersmoothing algorithm, and/or an exponential smoothing algorithm. In somecircumstances, these smoothing algorithms eliminate narrow spikes ordips in the intensities of the swipe contact for purposes of determininga characteristic intensity.

The intensity of a contact on the touch-sensitive surface ischaracterized relative to one or more intensity thresholds, such as acontact-detection intensity threshold, a light press intensitythreshold, a deep press intensity threshold, and/or one or more otherintensity thresholds. In some embodiments, the light press intensitythreshold corresponds to an intensity at which the device will performoperations typically associated with clicking a button of a physicalmouse or a trackpad. In some embodiments, the deep press intensitythreshold corresponds to an intensity at which the device will performoperations that are different from operations typically associated withclicking a button of a physical mouse or a trackpad. In someembodiments, when a contact is detected with a characteristic intensitybelow the light press intensity threshold (e.g., and above a nominalcontact-detection intensity threshold below which the contact is nolonger detected), the device will move a focus selector in accordancewith movement of the contact on the touch-sensitive surface withoutperforming an operation associated with the light press intensitythreshold or the deep press intensity threshold. Generally, unlessotherwise stated, these intensity thresholds are consistent betweendifferent sets of user interface figures.

An increase of characteristic intensity of the contact from an intensitybelow the light press intensity threshold to an intensity between thelight press intensity threshold and the deep press intensity thresholdis sometimes referred to as a “light press” input. An increase ofcharacteristic intensity of the contact from an intensity below the deeppress intensity threshold to an intensity above the deep press intensitythreshold is sometimes referred to as a “deep press” input. An increaseof characteristic intensity of the contact from an intensity below thecontact-detection intensity threshold to an intensity between thecontact-detection intensity threshold and the light press intensitythreshold is sometimes referred to as detecting the contact on thetouch-surface. A decrease of characteristic intensity of the contactfrom an intensity above the contact-detection intensity threshold to anintensity below the contact-detection intensity threshold is sometimesreferred to as detecting liftoff of the contact from the touch-surface.In some embodiments, the contact-detection intensity threshold is zero.In some embodiments, the contact-detection intensity threshold isgreater than zero.

In some embodiments described herein, one or more operations areperformed in response to detecting a gesture that includes a respectivepress input or in response to detecting the respective press inputperformed with a respective contact (or a plurality of contacts), wherethe respective press input is detected based at least in part ondetecting an increase in intensity of the contact (or plurality ofcontacts) above a press-input intensity threshold. In some embodiments,the respective operation is performed in response to detecting theincrease in intensity of the respective contact above the press-inputintensity threshold (e.g., a “down stroke” of the respective pressinput). In some embodiments, the press input includes an increase inintensity of the respective contact above the press-input intensitythreshold and a subsequent decrease in intensity of the contact belowthe press-input intensity threshold, and the respective operation isperformed in response to detecting the subsequent decrease in intensityof the respective contact below the press-input threshold (e.g., an “upstroke” of the respective press input).

In some embodiments, the device employs intensity hysteresis to avoidaccidental inputs sometimes termed “jitter,” where the device defines orselects a hysteresis intensity threshold with a predefined relationshipto the press-input intensity threshold (e.g., the hysteresis intensitythreshold is X intensity units lower than the press-input intensitythreshold or the hysteresis intensity threshold is 75%, 90%, or somereasonable proportion of the press-input intensity threshold). Thus, insome embodiments, the press input includes an increase in intensity ofthe respective contact above the press-input intensity threshold and asubsequent decrease in intensity of the contact below the hysteresisintensity threshold that corresponds to the press-input intensitythreshold, and the respective operation is performed in response todetecting the subsequent decrease in intensity of the respective contactbelow the hysteresis intensity threshold (e.g., an “up stroke” of therespective press input). Similarly, in some embodiments, the press inputis detected only when the device detects an increase in intensity of thecontact from an intensity at or below the hysteresis intensity thresholdto an intensity at or above the press-input intensity threshold and,optionally, a subsequent decrease in intensity of the contact to anintensity at or below the hysteresis intensity, and the respectiveoperation is performed in response to detecting the press input (e.g.,the increase in intensity of the contact or the decrease in intensity ofthe contact, depending on the circumstances).

For ease of explanation, the descriptions of operations performed inresponse to a press input associated with a press-input intensitythreshold or in response to a gesture including the press input are,optionally, triggered in response to detecting either: an increase inintensity of a contact above the press-input intensity threshold, anincrease in intensity of a contact from an intensity below thehysteresis intensity threshold to an intensity above the press-inputintensity threshold, a decrease in intensity of the contact below thepress-input intensity threshold, and/or a decrease in intensity of thecontact below the hysteresis intensity threshold corresponding to thepress-input intensity threshold. Additionally, in examples where anoperation is described as being performed in response to detecting adecrease in intensity of a contact below the press-input intensitythreshold, the operation is, optionally, performed in response todetecting a decrease in intensity of the contact below a hysteresisintensity threshold corresponding to, and lower than, the press-inputintensity threshold.

3. Digital Assistant System

FIG. 7A illustrates a block diagram of digital assistant system 700 inaccordance with various examples. In some examples, digital assistantsystem 700 is implemented on a standalone computer system. In someexamples, digital assistant system 700 is distributed across multiplecomputers. In some examples, some of the modules and functions of thedigital assistant are divided into a server portion and a clientportion, where the client portion resides on one or more user devices(e.g., devices 104, 122, 200, 400, or 600) and communicates with theserver portion (e.g., server system 108) through one or more networks,e.g., as shown in FIG. 1. In some examples, digital assistant system 700is an implementation of server system 108 (and/or DA server 106) shownin FIG. 1. It should be noted that digital assistant system 700 is onlyone example of a digital assistant system, and that digital assistantsystem 700 can have more or fewer components than shown, can combine twoor more components, or can have a different configuration or arrangementof the components. The various components shown in FIG. 7A areimplemented in hardware, software instructions for execution by one ormore processors, firmware, including one or more signal processingand/or application specific integrated circuits, or a combinationthereof.

Digital assistant system 700 includes memory 702, one or more processors704, input/output (I/O) interface 706, and network communicationsinterface 708. These components can communicate with one another overone or more communication buses or signal lines 710.

In some examples, memory 702 includes a non-transitory computer-readablemedium, such as high-speed random access memory and/or a non-volatilecomputer-readable storage medium (e.g., one or more magnetic diskstorage devices, flash memory devices, or other non-volatile solid-statememory devices).

In some examples, I/O interface 706 couples input/output devices 716 ofdigital assistant system 700, such as displays, keyboards, touchscreens, and microphones, to user interface module 722. I/O interface706, in conjunction with user interface module 722, receives user inputs(e.g., voice input, keyboard inputs, touch inputs, etc.) and processesthem accordingly. In some examples, e.g., when the digital assistant isimplemented on a standalone user device, digital assistant system 700includes any of the components and I/O communication interfacesdescribed with respect to devices 200, 400, or 600 in FIGS. 2A, 4, 6A-B,and 10. In some examples, digital assistant system 700 represents theserver portion of a digital assistant implementation, and can interactwith the user through a client-side portion residing on a user device(e.g., devices 104, 200, 400, or 600).

In some examples, the network communications interface 708 includeswired communication port(s) 712 and/or wireless transmission andreception circuitry 714. The wired communication port(s) receives andsend communication signals via one or more wired interfaces, e.g.,Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wirelesscircuitry 714 receives and sends RF signals and/or optical signalsfrom/to communications networks and other communications devices. Thewireless communications use any of a plurality of communicationsstandards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA,Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communicationprotocol. Network communications interface 708 enables communicationbetween digital assistant system 700 with networks, such as theInternet, an intranet, and/or a wireless network, such as a cellulartelephone network, a wireless local area network (LAN), and/or ametropolitan area network (MAN), and other devices.

In some examples, memory 702, or the computer-readable storage media ofmemory 702, stores programs, modules, instructions, and data structuresincluding all or a subset of: operating system 718, communicationsmodule 720, user interface module 722, one or more applications 724, anddigital assistant module 726. In particular, memory 702, or thecomputer-readable storage media of memory 702, stores instructions forperforming the processes described below. One or more processors 704execute these programs, modules, and instructions, and reads/writesfrom/to the data structures.

Operating system 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communications between varioushardware, firmware, and software components.

Communications module 720 facilitates communications between digitalassistant system 700 with other devices over network communicationsinterface 708. For example, communications module 720 communicates withRF circuitry 208 of electronic devices such as devices 200, 400, and 600shown in FIGS. 2A, 4, 6A-B, respectively. Communications module 720 alsoincludes various components for handling data received by wirelesscircuitry 714 and/or wired communications port 712.

User interface module 722 receives commands and/or inputs from a uservia I/O interface 706 (e.g., from a keyboard, touch screen, pointingdevice, controller, and/or microphone), and generate user interfaceobjects on a display. User interface module 722 also prepares anddelivers outputs (e.g., speech, sound, animation, text, icons,vibrations, haptic feedback, light, etc.) to the user via the I/Ointerface 706 (e.g., through displays, audio channels, speakers,touch-pads, etc.).

Applications 724 include programs and/or modules that are configured tobe executed by one or more processors 704. For example, if the digitalassistant system is implemented on a standalone user device,applications 724 include user applications, such as games, a calendarapplication, a navigation application, or an email application. Ifdigital assistant system 700 is implemented on a server, applications724 include resource management applications, diagnostic applications,or scheduling applications, for example.

Memory 702 also stores digital assistant module 726 (or the serverportion of a digital assistant). In some examples, digital assistantmodule 726 includes the following sub-modules, or a subset or supersetthereof: input/output processing module 728, speech-to-text (STT)processing module 730, natural language processing module 732, dialogueflow processing module 734, task flow processing module 736, serviceprocessing module 738, and speech synthesis processing module 740. Eachof these modules has access to one or more of the following systems ordata and models of the digital assistant module 726, or a subset orsuperset thereof: ontology 760, vocabulary index 744, user data 748,task flow models 754, service models 756, and ASR systems 758.

In some examples, using the processing modules, data, and modelsimplemented in digital assistant module 726, the digital assistant canperform at least some of the following: converting speech input intotext; identifying a user's intent expressed in a natural language inputreceived from the user; actively eliciting and obtaining informationneeded to fully infer the user's intent (e.g., by disambiguating words,games, intentions, etc.); determining the task flow for fulfilling theinferred intent; and executing the task flow to fulfill the inferredintent.

In some examples, as shown in FIG. 7B, I/O processing module 728interacts with the user through I/O devices 716 in FIG. 7A or with auser device (e.g., devices 104, 200, 400, or 600) through networkcommunications interface 708 in FIG. 7A to obtain user input (e.g., aspeech input) and to provide responses (e.g., as speech outputs) to theuser input. I/O processing module 728 optionally obtains contextualinformation associated with the user input from the user device, alongwith or shortly after the receipt of the user input. The contextualinformation includes user-specific data, vocabulary, and/or preferencesrelevant to the user input. In some examples, the contextual informationalso includes software and hardware states of the user device at thetime the user request is received, and/or information related to thesurrounding environment of the user at the time that the user requestwas received. In some examples, I/O processing module 728 also sendsfollow-up questions to, and receive answers from, the user regarding theuser request. When a user request is received by I/O processing module728 and the user request includes speech input, I/O processing module728 forwards the speech input to STT processing module 730 (or speechrecognizer) for speech-to-text conversions.

STT processing module 730 includes one or more ASR systems 758. The oneor more ASR systems 758 can process the speech input that is receivedthrough I/O processing module 728 to produce a recognition result. EachASR system 758 includes a front-end speech pre-processor. The front-endspeech pre-processor extracts representative features from the speechinput. For example, the front-end speech pre-processor performs aFourier transform on the speech input to extract spectral features thatcharacterize the speech input as a sequence of representativemulti-dimensional vectors. Further, each ASR system 758 includes one ormore speech recognition models (e.g., acoustic models and/or languagemodels) and implements one or more speech recognition engines. Examplesof speech recognition models include Hidden Markov Models,Gaussian-Mixture Models, Deep Neural Network Models, n-gram languagemodels, and other statistical models. Examples of speech recognitionengines include the dynamic time warping based engines and weightedfinite-state transducers (WFST) based engines. The one or more speechrecognition models and the one or more speech recognition engines areused to process the extracted representative features of the front-endspeech pre-processor to produce intermediate recognitions results (e.g.,phonemes, phonemic strings, and sub-words), and ultimately, textrecognition results (e.g., words, word strings, or sequence of tokens).In some examples, the speech input is processed at least partially by athird-party service or on the user's device (e.g., device 104, 200, 400,or 600) to produce the recognition result. Once STT processing module730 produces recognition results containing a text string (e.g., words,or sequence of words, or sequence of tokens), the recognition result ispassed to natural language processing module 732 for intent deduction.In some examples, STT processing module 730 produces multiple candidatetext representations of the speech input. Each candidate textrepresentation is a sequence of words or tokens corresponding to thespeech input. In some examples, each candidate text representation isassociated with a speech recognition confidence score. Based on thespeech recognition confidence scores, STT processing module 730 ranksthe candidate text representations and provides the n-best (e.g., nhighest ranked) candidate text representation(s) to natural languageprocessing module 732 for intent deduction, where n is a predeterminedinteger greater than zero. For example, in one example, only the highestranked (n=1) candidate text representation is passed to natural languageprocessing module 732 for intent deduction. In another example, the fivehighest ranked (n=5) candidate text representations are passed tonatural language processing module 732 for intent deduction.

More details on the speech-to-text processing are described in U.S.Utility application Ser. No. 13/236,942 for “Consolidating SpeechRecognition Results,” filed on Sep. 20, 2011, the entire disclosure ofwhich is incorporated herein by reference.

In some examples, STT processing module 730 includes and/or accesses avocabulary of recognizable words via phonetic alphabet conversion module731. Each vocabulary word is associated with one or more candidatepronunciations of the word represented in a speech recognition phoneticalphabet. In particular, the vocabulary of recognizable words includes aword that is associated with a plurality of candidate pronunciations.For example, the vocabulary includes the word “tomato” that isassociated with the candidate pronunciations of /

/ and /

/. Further, vocabulary words are associated with custom candidatepronunciations that are based on previous speech inputs from the user.Such custom candidate pronunciations are stored in STT processing module730 and are associated with a particular user via the user's profile onthe device. In some examples, the candidate pronunciations for words aredetermined based on the spelling of the word and one or more linguisticand/or phonetic rules. In some examples, the candidate pronunciationsare manually generated, e.g., based on known canonical pronunciations.

In some examples, the candidate pronunciations are ranked based on thecommonness of the candidate pronunciation. For example, the candidatepronunciation /

/ is ranked higher than /

/, because the former is a more commonly used pronunciation (e.g., amongall users, for users in a particular geographical region, or for anyother appropriate subset of users). In some examples, candidatepronunciations are ranked based on whether the candidate pronunciationis a custom candidate pronunciation associated with the user. Forexample, custom candidate pronunciations are ranked higher thancanonical candidate pronunciations. This can be useful for recognizingproper nouns having a unique pronunciation that deviates from canonicalpronunciation. In some examples, candidate pronunciations are associatedwith one or more speech characteristics, such as geographic origin,nationality, or ethnicity. For example, the candidate pronunciation /

/ is associated with the United States, whereas the candidatepronunciation /

/ is associated with Great Britain. Further, the rank of the candidatepronunciation is based on one or more characteristics (e.g., geographicorigin, nationality, ethnicity, etc.) of the user stored in the user'sprofile on the device. For example, it can be determined from the user'sprofile that the user is associated with the United States. Based on theuser being associated with the United States, the candidatepronunciation /

/ (associated with the United States) is ranked higher than thecandidate pronunciation /

/ (associated with Great Britain). In some examples, one of the rankedcandidate pronunciations is selected as a predicted pronunciation (e.g.,the most likely pronunciation).

When a speech input is received, STT processing module 730 is used todetermine the phonemes corresponding to the speech input (e.g., using anacoustic model), and then attempt to determine words that match thephonemes (e.g., using a language model). For example, if STT processingmodule 730 first identifies the sequence of phonemes /

/ corresponding to a portion of the speech input, it can then determine,based on vocabulary index 744, that this sequence corresponds to theword “tomato.”

In some examples, STT processing module 730 uses approximate matchingtechniques to determine words in an utterance. Thus, for example, theSTT processing module 730 determines that the sequence of phonemes /

/ corresponds to the word “tomato,” even if that particular sequence ofphonemes is not one of the candidate sequence of phonemes for that word.

Natural language processing module 732 (“natural language processor”) ofthe digital assistant takes the n-best candidate text representation(s)(“word sequence(s)” or “token sequence(s)”) generated by STT processingmodule 730, and attempts to associate each of the candidate textrepresentations with one or more “actionable intents” recognized by thedigital assistant. An “actionable intent” (or “user intent”) representsa task that can be performed by the digital assistant, and can have anassociated task flow implemented in task flow models 754. The associatedtask flow is a series of programmed actions and steps that the digitalassistant takes in order to perform the task. The scope of a digitalassistant's capabilities is dependent on the number and variety of taskflows that have been implemented and stored in task flow models 754, orin other words, on the number and variety of “actionable intents” thatthe digital assistant recognizes. The effectiveness of the digitalassistant, however, also dependents on the assistant's ability to inferthe correct “actionable intent(s)” from the user request expressed innatural language.

In some examples, in addition to the sequence of words or tokensobtained from STT processing module 730, natural language processingmodule 732 also receives contextual information associated with the userrequest, e.g., from I/O processing module 728. The natural languageprocessing module 732 optionally uses the contextual information toclarify, supplement, and/or further define the information contained inthe candidate text representations received from STT processing module730. The contextual information includes, for example, user preferences,hardware, and/or software states of the user device, sensor informationcollected before, during, or shortly after the user request, priorinteractions (e.g., dialogue) between the digital assistant and theuser, and the like. As described herein, contextual information is, insome examples, dynamic, and changes with time, location, content of thedialogue, and other factors.

In some examples, the natural language processing is based on, e.g.,ontology 760. Ontology 760 is a hierarchical structure containing manynodes, each node representing either an “actionable intent” or a“property” relevant to one or more of the “actionable intents” or other“properties.” As noted above, an “actionable intent” represents a taskthat the digital assistant is capable of performing, i.e., it is“actionable” or can be acted on. A “property” represents a parameterassociated with an actionable intent or a sub-aspect of anotherproperty. A linkage between an actionable intent node and a propertynode in ontology 760 defines how a parameter represented by the propertynode pertains to the task represented by the actionable intent node.

In some examples, ontology 760 is made up of actionable intent nodes andproperty nodes. Within ontology 760, each actionable intent node islinked to one or more property nodes either directly or through one ormore intermediate property nodes. Similarly, each property node islinked to one or more actionable intent nodes either directly or throughone or more intermediate property nodes. For example, as shown in FIG.7C, ontology 760 includes a “restaurant reservation” node (i.e., anactionable intent node). Property nodes “restaurant,” “date/time” (forthe reservation), and “party size” are each directly linked to theactionable intent node (i.e., the “restaurant reservation” node).

In addition, property nodes “cuisine,” “price range,” “phone number,”and “location” are sub-nodes of the property node “restaurant,” and areeach linked to the “restaurant reservation” node (i.e., the actionableintent node) through the intermediate property node “restaurant.” Foranother example, as shown in FIG. 7C, ontology 760 also includes a “setreminder” node (i.e., another actionable intent node). Property nodes“date/time” (for setting the reminder) and “subject” (for the reminder)are each linked to the “set reminder” node. Since the property“date/time” is relevant to both the task of making a restaurantreservation and the task of setting a reminder, the property node“date/time” is linked to both the “restaurant reservation” node and the“set reminder” node in ontology 760.

An actionable intent node, along with its linked property nodes, isdescribed as a “domain.” In the present discussion, each domain isassociated with a respective actionable intent, and refers to the groupof nodes (and the relationships there between) associated with theparticular actionable intent. For example, ontology 760 shown in FIG. 7Cincludes an example of restaurant reservation domain 762 and an exampleof reminder domain 764 within ontology 760. The restaurant reservationdomain includes the actionable intent node “restaurant reservation,”property nodes “restaurant,” “date/time,” and “party size,” andsub-property nodes “cuisine,” “price range,” “phone number,” and“location.” Reminder domain 764 includes the actionable intent node “setreminder,” and property nodes “subject” and “date/time.” In someexamples, ontology 760 is made up of many domains. Each domain sharesone or more property nodes with one or more other domains. For example,the “date/time” property node is associated with many different domains(e.g., a scheduling domain, a travel reservation domain, a movie ticketdomain, etc.), in addition to restaurant reservation domain 762 andreminder domain 764.

While FIG. 7C illustrates two example domains within ontology 760, otherdomains include, for example, “find a movie,” “initiate a phone call,”“find directions,” “schedule a meeting,” “send a message,” and “providean answer to a question,” “read a list,” “providing navigationinstructions,” “provide instructions for a task” and so on. A “send amessage” domain is associated with a “send a message” actionable intentnode, and further includes property nodes such as “recipient(s),”“message type,” and “message body.” The property node “recipient” isfurther defined, for example, by the sub-property nodes such as“recipient name” and “message address.”

In some examples, ontology 760 includes all the domains (and henceactionable intents) that the digital assistant is capable ofunderstanding and acting upon. In some examples, ontology 760 ismodified, such as by adding or removing entire domains or nodes, or bymodifying relationships between the nodes within the ontology 760.

In some examples, nodes associated with multiple related actionableintents are clustered under a “super domain” in ontology 760. Forexample, a “travel” super-domain includes a cluster of property nodesand actionable intent nodes related to travel. The actionable intentnodes related to travel includes “airline reservation,” “hotelreservation,” “car rental,” “get directions,” “find points of interest,”and so on. The actionable intent nodes under the same super domain(e.g., the “travel” super domain) have many property nodes in common.For example, the actionable intent nodes for “airline reservation,”“hotel reservation,” “car rental,” “get directions,” and “find points ofinterest” share one or more of the property nodes “start location,”“destination,” “departure date/time,” “arrival date/time,” and “partysize.”

In some examples, each node in ontology 760 is associated with a set ofwords and/or phrases that are relevant to the property or actionableintent represented by the node. The respective set of words and/orphrases associated with each node are the so-called “vocabulary”associated with the node. The respective set of words and/or phrasesassociated with each node are stored in vocabulary index 744 inassociation with the property or actionable intent represented by thenode. For example, returning to FIG. 7B, the vocabulary associated withthe node for the property of “restaurant” includes words such as “food,”“drinks,” “cuisine,” “hungry,” “eat,” “pizza,” “fast food,” “meal,” andso on. For another example, the vocabulary associated with the node forthe actionable intent of “initiate a phone call” includes words andphrases such as “call,” “phone,” “dial,” “ring,” “call this number,”“make a call to,” and so on. The vocabulary index 744 optionallyincludes words and phrases in different languages.

Natural language processing module 732 receives the candidate textrepresentations (e.g., text string(s) or token sequence(s)) from STTprocessing module 730, and for each candidate representation, determineswhat nodes are implicated by the words in the candidate textrepresentation. In some examples, if a word or phrase in the candidatetext representation is found to be associated with one or more nodes inontology 760 (via vocabulary index 744), the word or phrase “triggers”or “activates” those nodes. Based on the quantity and/or relativeimportance of the activated nodes, natural language processing module732 selects one of the actionable intents as the task that the userintended the digital assistant to perform. In some examples, the domainthat has the most “triggered” nodes is selected. In some examples, thedomain having the highest confidence value (e.g., based on the relativeimportance of its various triggered nodes) is selected. In someexamples, the domain is selected based on a combination of the numberand the importance of the triggered nodes. In some examples, additionalfactors are considered in selecting the node as well, such as whetherthe digital assistant has previously correctly interpreted a similarrequest from a user.

User data 748 includes user-specific information, such as user-specificvocabulary, user preferences, user address, user's default and secondarylanguages, user's contact list, and other short-term or long-terminformation for each user. In some examples, natural language processingmodule 732 uses the user-specific information to supplement theinformation contained in the user input to further define the userintent. For example, for a user request “invite my friends to mybirthday party,” natural language processing module 732 is able toaccess user data 748 to determine who the “friends” are and when andwhere the “birthday party” would be held, rather than requiring the userto provide such information explicitly in his/her request.

It should be recognized that in some examples, natural languageprocessing module 732 is implemented using one or more machine learningmechanisms (e.g., neural networks). In particular, the one or moremachine learning mechanisms are configured to receive a candidate textrepresentation and contextual information associated with the candidatetext representation. Based on the candidate text representation and theassociated contextual information, the one or more machine learningmechanisms are configured to determine intent confidence scores over aset of candidate actionable intents. Natural language processing module732 can select one or more candidate actionable intents from the set ofcandidate actionable intents based on the determined intent confidencescores. In some examples, an ontology (e.g., ontology 760) is also usedto select the one or more candidate actionable intents from the set ofcandidate actionable intents.

Other details of searching an ontology based on a token string aredescribed in U.S. Utility application Ser. No. 12/341,743 for “Methodand Apparatus for Searching Using An Active Ontology,” filed Dec. 22,2008, the entire disclosure of which is incorporated herein byreference.

In some examples, once natural language processing module 732 identifiesan actionable intent (or domain) based on the user request, naturallanguage processing module 732 generates a structured query to representthe identified actionable intent. In some examples, the structured queryincludes parameters for one or more nodes within the domain for theactionable intent, and at least some of the parameters are populatedwith the specific information and requirements specified in the userrequest. For example, the user says “Make me a dinner reservation at asushi place at 7.” In this case, natural language processing module 732is able to correctly identify the actionable intent to be “restaurantreservation” based on the user input. According to the ontology, astructured query for a “restaurant reservation” domain includesparameters such as {Cuisine}, {Time}, {Date}, {Party Size}, and thelike. In some examples, based on the speech input and the text derivedfrom the speech input using STT processing module 730, natural languageprocessing module 732 generates a partial structured query for therestaurant reservation domain, where the partial structured queryincludes the parameters {Cuisine=“Sushi”} and {Time=“7 pm”}. However, inthis example, the user's utterance contains insufficient information tocomplete the structured query associated with the domain. Therefore,other necessary parameters such as {Party Size} and {Date} are notspecified in the structured query based on the information currentlyavailable. In some examples, natural language processing module 732populates some parameters of the structured query with receivedcontextual information. For example, in some examples, if the userrequested a sushi restaurant “near me,” natural language processingmodule 732 populates a {location} parameter in the structured query withGPS coordinates from the user device.

In some examples, natural language processing module 732 identifiesmultiple candidate actionable intents for each candidate textrepresentation received from STT processing module 730. Further, in someexamples, a respective structured query (partial or complete) isgenerated for each identified candidate actionable intent. Naturallanguage processing module 732 determines an intent confidence score foreach candidate actionable intent and ranks the candidate actionableintents based on the intent confidence scores. In some examples, naturallanguage processing module 732 passes the generated structured query (orqueries), including any completed parameters, to task flow processingmodule 736 (“task flow processor”). In some examples, the structuredquery (or queries) for the m-best (e.g., m highest ranked) candidateactionable intents are provided to task flow processing module 736,where m is a predetermined integer greater than zero. In some examples,the structured query (or queries) for the m-best candidate actionableintents are provided to task flow processing module 736 with thecorresponding candidate text representation(s).

Other details of inferring a user intent based on multiple candidateactionable intents determined from multiple candidate textrepresentations of a speech input are described in U.S. Utilityapplication Ser. No. 14/298,725 for “System and Method for InferringUser Intent From Speech Inputs,” filed Jun. 6, 2014, the entiredisclosure of which is incorporated herein by reference.

Task flow processing module 736 is configured to receive the structuredquery (or queries) from natural language processing module 732, completethe structured query, if necessary, and perform the actions required to“complete” the user's ultimate request. In some examples, the variousprocedures necessary to complete these tasks are provided in task flowmodels 754. In some examples, task flow models 754 include proceduresfor obtaining additional information from the user and task flows forperforming actions associated with the actionable intent.

As described above, in order to complete a structured query, task flowprocessing module 736 needs to initiate additional dialogue with theuser in order to obtain additional information, and/or disambiguatepotentially ambiguous utterances. When such interactions are necessary,task flow processing module 736 invokes dialogue flow processing module734 to engage in a dialogue with the user. In some examples, dialogueflow processing module 734 determines how (and/or when) to ask the userfor the additional information and receives and processes the userresponses. The questions are provided to and answers are received fromthe users through I/O processing module 728. In some examples, dialogueflow processing module 734 presents dialogue output to the user viaaudio and/or visual output, and receives input from the user via spokenor physical (e.g., clicking) responses. Continuing with the exampleabove, when task flow processing module 736 invokes dialogue flowprocessing module 734 to determine the “party size” and “date”information for the structured query associated with the domain“restaurant reservation,” dialogue flow processing module 734 generatesquestions such as “For how many people?” and “On which day?” to pass tothe user. Once answers are received from the user, dialogue flowprocessing module 734 then populates the structured query with themissing information, or pass the information to task flow processingmodule 736 to complete the missing information from the structuredquery.

Once task flow processing module 736 has completed the structured queryfor an actionable intent, task flow processing module 736 proceeds toperform the ultimate task associated with the actionable intent.Accordingly, task flow processing module 736 executes the steps andinstructions in the task flow model according to the specific parameterscontained in the structured query. For example, the task flow model forthe actionable intent of “restaurant reservation” includes steps andinstructions for contacting a restaurant and actually requesting areservation for a particular party size at a particular time. Forexample, using a structured query such as: {restaurant reservation,restaurant=ABC Café, date=3/12/2012, time=7 pm, party size=5}, task flowprocessing module 736 performs the steps of: (1) logging onto a serverof the ABC Café or a restaurant reservation system such as OPENTABLE®,(2) entering the date, time, and party size information in a form on thewebsite, (3) submitting the form, and (4) making a calendar entry forthe reservation in the user's calendar.

In some examples, task flow processing module 736 employs the assistanceof service processing module 738 (“service processing module”) tocomplete a task requested in the user input or to provide aninformational answer requested in the user input. For example, serviceprocessing module 738 acts on behalf of task flow processing module 736to make a phone call, set a calendar entry, invoke a map search, invokeor interact with other user applications installed on the user device,and invoke or interact with third-party services (e.g., a restaurantreservation portal, a social networking website, a banking portal,etc.). In some examples, the protocols and application programminginterfaces (API) required by each service are specified by a respectiveservice model among service models 756. Service processing module 738accesses the appropriate service model for a service and generatesrequests for the service in accordance with the protocols and APIsrequired by the service according to the service model.

For example, if a restaurant has enabled an online reservation service,the restaurant submits a service model specifying the necessaryparameters for making a reservation and the APIs for communicating thevalues of the necessary parameter to the online reservation service.When requested by task flow processing module 736, service processingmodule 738 establishes a network connection with the online reservationservice using the web address stored in the service model, and sends thenecessary parameters of the reservation (e.g., time, date, party size)to the online reservation interface in a format according to the API ofthe online reservation service.

In some examples, natural language processing module 732, dialogue flowprocessing module 734, and task flow processing module 736 are usedcollectively and iteratively to infer and define the user's intent,obtain information to further clarify and refine the user intent, andfinally generate a response (i.e., an output to the user, or thecompletion of a task) to fulfill the user's intent. The generatedresponse is a dialogue response to the speech input that at leastpartially fulfills the user's intent. Further, in some examples, thegenerated response is output as a speech output. In these examples, thegenerated response is sent to speech synthesis processing module 740(e.g., speech synthesizer) where it can be processed to synthesize thedialogue response in speech form. In yet other examples, the generatedresponse is data content relevant to satisfying a user request in thespeech input.

In examples where task flow processing module 736 receives multiplestructured queries from natural language processing module 732, taskflow processing module 736 initially processes the first structuredquery of the received structured queries to attempt to complete thefirst structured query and/or execute one or more tasks or actionsrepresented by the first structured query. In some examples, the firststructured query corresponds to the highest ranked actionable intent. Inother examples, the first structured query is selected from the receivedstructured queries based on a combination of the corresponding speechrecognition confidence scores and the corresponding intent confidencescores. In some examples, if task flow processing module 736 encountersan error during processing of the first structured query (e.g., due toan inability to determine a necessary parameter), the task flowprocessing module 736 can proceed to select and process a secondstructured query of the received structured queries that corresponds toa lower ranked actionable intent. The second structured query isselected, for example, based on the speech recognition confidence scoreof the corresponding candidate text representation, the intentconfidence score of the corresponding candidate actionable intent, amissing necessary parameter in the first structured query, or anycombination thereof.

Speech synthesis processing module 740 is configured to synthesizespeech outputs for presentation to the user. Speech synthesis processingmodule 740 synthesizes speech outputs based on text provided by thedigital assistant. For example, the generated dialogue response is inthe form of a text string. Speech synthesis processing module 740converts the text string to an audible speech output. Speech synthesisprocessing module 740 uses any appropriate speech synthesis technique inorder to generate speech outputs from text, including, but not limited,to concatenative synthesis, unit selection synthesis, diphone synthesis,domain-specific synthesis, formant synthesis, articulatory synthesis,hidden Markov model (HMM) based synthesis, and sinewave synthesis. Insome examples, speech synthesis processing module 740 is configured tosynthesize individual words based on phonemic strings corresponding tothe words. For example, a phonemic string is associated with a word inthe generated dialogue response. The phonemic string is stored inmetadata associated with the word. Speech synthesis processing module740 is configured to directly process the phonemic string in themetadata to synthesize the word in speech form.

In some examples, instead of (or in addition to) using speech synthesisprocessing module 740, speech synthesis is performed on a remote device(e.g., the server system 108), and the synthesized speech is sent to theuser device for output to the user. For example, this can occur in someimplementations where outputs for a digital assistant are generated at aserver system. And because server systems generally have more processingpower or resources than a user device, it is possible to obtain higherquality speech outputs than would be practical with client-sidesynthesis.

Additional details on digital assistants can be found in the U.S.Utility application Ser. No. 12/987,982, entitled “Intelligent AutomatedAssistant,” filed Jan. 10, 2011, and U.S. Utility application Ser. No.13/251,088, entitled “Generating and Processing Task Items ThatRepresent Tasks to Perform,” filed Sep. 30, 2011, the entire disclosuresof which are incorporated herein by reference.

4. Speculative Task Flow Execution

FIG. 8 illustrates a portion of digital assistant (DA) module 800,according to various examples. For simplicity, only a portion of DAmodule 800 is depicted and it should be recognized that DA module 800can include additional components. For example, DA module 800 caninclude one or more additional components described with respect to DAmodule 726 and DA module 800 can reside in memory 702 of DA system 700.Further, some components of DA module 800 can be similar orsubstantially identical to analogous components of DA module 726 andother components of DA module 800 can be implemented within or replacecomponents of DA module 726.

In some examples, similar to DA system 700, DA module 800 can beimplemented on a standalone computer system (e.g., devices 104, 122,200, 400, or 600) or distributed across multiple computers. For example,some of the modules and functions of DA module 800 are divided into aserver portion and a client portion, where the client portion resides onone or more user devices (e.g., devices 104, 122, 200, 400, or 600) andcommunicates with the server portion (e.g., server system 108) throughone or more networks, e.g., as shown in FIG. 1. As another example, allmodules and functions of DA module 800 reside on a single user device.

Input processing module 802 is configured to receive natural languageinput in audio or textual form. Input processing module 802 isconfigured to determine when the natural language input begins (starts)and/or ends. For example, for audio input, input processing module 802uses speech detection and endpointing techniques to determine whenspeech begins and ends in the audio input. As another example, fornatural language text input, input processing module 802 determines thatthe text input begins when a user begins providing the text, or when auser selects a displayed textual input field. In some examples, inputprocessing module 802 determines that the text input ends upon detectinguser input ending the text input, e.g., user input indicating the DAshould process the text input. If audio input is received, inputprocessing module 802 provides (e.g., streams) the audio input to STTprocessing module 804, e.g., upon detecting a start of speech in theaudio input. If text input is received, input processing module 802provides the text input to natural language processing module 806, e.g.,upon determining a start of the text input.

FIG. 9 illustrates a timeline and waveform for received natural languagespeech input 900, according to various examples. Input processing module802 determines that speech input 900, e.g., “translate hello toSpanish,” starts at start time 902 and ends at end time 908. Speechinput 900 includes speech portion 910 (“translate hello”) received fromstart time 902 to first time 904 and speech portion 912 (“translatehello to Spanish”) received from start time 902 to second time 906. Insome examples, input processing module 802 uses speech endpointingtechniques to determine end time 908. It will be appreciated that thedetermined end time of natural language input (e.g., end time 908) maybe after the user request completion time (e.g., second time 906). Forexample, some speech endpointing techniques may rely on detecting aperiod of relative silence after the speech ends to determine the endtime as when the period of relative silence ends.

STT processing module 804 is configured to receive audio input (e.g.,natural language speech input) from input processing module 802. STTprocessing module 804 is similar to or substantially identical to STTprocessing module 730. For example STT processing module 804 isconfigured to perform, in real-time while receiving the speech input,speech recognition. For example, STT processing module 804 determines atfirst time 904 (or shortly thereafter), the candidate text “translatehello” for speech portion 910. STT processing module 804 furtherdetermines, at second time 906 (or shortly thereafter), the candidatetext “translate hello to Spanish” for speech portion 912.

In some examples, STT processing module 804 determines multiplecandidate texts for a same portion of the speech input. In someexamples, STT processing module 804 assigns each determined candidatetext a respective speech recognition confidence score, e.g., accordingto speech recognition techniques known in the art. STT processing module804 ranks candidate texts according to their respective confidencescores. For example, for speech portion 910, STT processing module 804further determines the candidate text “translate yellow.” STT processingmodule 804 assigns “translate hello” a higher speech recognitionconfidence score than “translate yellow,” indicating that “translatehello” is more likely correct than “translate yellow” for speech portion910. Similarly, for speech portion 912, STT processing module 804further determines the text “translate yellow to Spanish.” STTprocessing module 804 assigns “translate hello to Spanish” a higherconfidence score than “translate yellow to Spanish.” STT processingmodule 804 provides each determined candidate text to natural languageprocessing module 806.

Natural language processing module 806 is configured to receive textinput and determine a corresponding representation of user intent. Forexample, natural language processing module 806 receives candidate textfrom STT processing module 804, e.g., in real-time as the candidate textis recognized. As another example, natural language processing module806 receives text from input processing module 802, e.g., in real-timeas the text is input. Natural language processing module 806 is similarto or substantially identical to natural language processing module 732.Natural language processing module 806 provides each determinedrepresentation of user intent to task flow processing module 808.

Each determined representation of user intent can include any type ofdata structure representing a user intent. For example, as discussedwith respect to FIGS. 7A-C, a domain and a corresponding structuredquery represent a user intent. For example, based on speech portion 910(and the candidate text “translate hello”), the domain is a translationdomain, e.g., a domain associated with actionable intents of translatingtext into various languages. The corresponding structured query includesthe parameter of {input text=“hello”}, e.g., indicating that the text totranslate is “hello.” In other examples, each determined representationof user intent includes another type of data structure. For example,based on speech portion 910, natural language processing module 806determines a parse tree representing the user intent to translate“hello.” In some examples, natural language processing module 806assigns each determined representation of user intent a respectiveconfidence score according to natural language processing techniquesknown in the art.

In some examples, natural language processing module 806 determinesmultiple representations of user intent corresponding to the sameportion of the natural language input. For example, module 806determines each of the multiple representations based respectively onmultiple candidate texts for the same portion, or based on a singlecandidate text for the same portion. As a specific example, in FIG. 9,based on speech portion 910 (and the candidate text “translate hello”),module 806 determines a representation of a user intent to translate“hello.” Based on speech portion 910 (and the candidate text “translateyellow”), module 806 determines a representation of a user intent totranslate “yellow.” Similarly, based on speech portion 912 (and thecandidate text “translate hello to Spanish”), module 806 determines arepresentation of a user intent to translate “hello” to Spanish. Basedon speech portion 912 (and the candidate text “translate yellow toSpanish”), module 806 determines a representation of a user intent totranslate “yellow” to Spanish. Module 806 assigns each determinedrepresentation of user intent a respective confidence score.

Task flow processing module 808 is configured to receive representationsof user intent. Based on the representations of user intent, task flowprocessing module 808 is configured to select and perform one or moreactions to satisfy a user request included in the natural languageinput. In some examples, task flow processing module 808 is implementedwithin task flow processing module 736. For example, some operationsdiscussed below with respect to module 808 may complement operationsperformed by module 736, while other operations discussed below withrespect to module 808 may replace operations of module 736. In someexamples, module 808 entirely replaces module 736. As discussed below,processing user intent representations using module 808 may reduce thetime a DA takes to respond to a user request (reduce DA latency) and mayincrease the accuracy of the DA's response.

Task flow processing module 808 includes task flow selection module 810and speculative execution module 812. Task flow selection module 810 andspeculative execution module 812 are configured to determine candidateactions that may each potentially satisfy the user request whenperformed. In some examples, the operations discussed below with respectto task flow selection module 810 and speculative execution module 812are at least partially performed prior to determining an end time ofnatural language input. Performing such operations prior to determiningthe end time may reduce DA latency, as some processing of the userintent representations may occur prior to the end time, e.g., comparedto processing the user intent representations after determining the endtime.

Task flow selection module 810 is configured to receive representationsof user intent and determine (select) respective corresponding taskflows. Each task flow includes a series of programmed actions and stepstaken to satisfy (or as part of satisfying) a respective user intent.Accordingly, the number of different task flows available to task flowselection module 810 may encompass the scope of the DA's capabilities.In some examples, task flow selection module 810 stores all theavailable task flows. For example, task flow selection module 810implements task flow models 754. Example task flows include a task flowfor performing translations into various languages, for determiningwhether an entity corresponds to a name in the user's contacts, formaking phone calls, for making restaurant reservations, for providingscores of sports games, for providing weather information, for providinginformation associated with a flight reservation, and so forth. Taskflow selection module 810 is further configured to provide eachdetermined task flow to speculative execution module 812.

In some examples, task flow selection module 810 selects each task flowusing a mapping associating each user intent representation with therespective task flow, e.g., as discussed with respect to FIGS. 7A-C. Insome examples, as discussed further below with respect to FIG. 11,module 810 selects a task flow according to instructions obtained byexecuting another task flow. For example, if executing a first task flowdetermines that a necessary value of the first task flow is notspecified, the first task flow may instruct module 810 to provide asecond task flow (e.g., a sub-task flow) configured to obtain the value.

In the example of FIG. 9, for the representation of user intent (basedon speech portion 910) to translate “hello,” module 810 selects a taskflow for performing translations (perform translation task flow). Forthe representation of user intent (based on speech portion 912) totranslate “hello” to Spanish, module 810 similarly selects the performtranslation task flow. In some examples, module 810 further selects theperform translation task flow for the representation of user intent(based on speech portion 910) to translate “yellow” and for therepresentation of user intent (based on speech portion 912) to translate“yellow” to Spanish. Accordingly, module 810 may select four differentinstances of the perform translation task flow, one for each of the fourrepresentations of user intent.

Speculative execution module 812 is configured to receive each task flowfrom module 810 and execute each task flow. As discussed below,executing a task flow includes performing the programmed actions/stepsof the task flow to obtain an executable object representing a candidateaction to be performed. As further discussed below, executing each taskflow using speculative execution module 812 may evaluate each task flowto identify the task flow(s) resulting in correct candidate action(s)performed to satisfy a user request.

In some examples, speculative execution module 812 executes, partiallyor completely, each task flow before the end time of the naturallanguage input. For example, module 812 executes each task flow uponreceiving the task flow. Module 812 executes the task flows in parallelor sequentially (e.g., executing one task flow after another task flowoutputs an executable object). As a particular example, the two performtranslation task flows corresponding to the two user intentrepresentations based on speech portion 910 are determined and receivedshortly after first time 904 (and before end time 908). Speculativeexecution module 812 thus executes the two perform translation taskflows at least partially between first time 904 and end time 908.Similarly, the perform translation task flows corresponding to the twouser intent representations based on speech portion 912 are determinedand received shortly after the second time 906 (and before end time908). Module 812 thus executes the two (other) perform translation taskflows at least partially between second time 906 and end time 908.

In this manner, speculative execution module 812 is configured toexecute task flows speculatively, e.g., as each executed task flow mightnot represent the complete user request and/or might not represent acorrect interpretation of the user request. For example, the task flowcorresponding to the user intent to translate “hello” does not representthe completed user request of translating “hello” to Spanish.

Because module 812 executes each task flow speculatively, it may beundesirable for execution of a task flow to cause an electronic deviceto provide user-perceptible output. For example, the user-perceptibleoutput might be incorrect (as the corresponding task flow might beincorrect). Accordingly, in some examples, executing a task flowincludes executing the task flow without providing, by the device,user-perceptible output. User-perceptible output describes output thatwould otherwise be automatically provided responsive to the naturallanguage input, e.g., provided without the user providing further inputafter the natural language input. Example user-perceptible outputincludes audio output (e.g., a spoken response to natural languageinput) and displayed output (e.g., a displayed response to the naturallanguage input). As discussed below, when execution of a task flowdetermines the next action to be performed includes providinguser-perceptible output, module 812 may not perform the action, andinstead output an executable object representing the action.

Further details about how speculative execution module 812 executes andevaluates task flows are now discussed with respect to updating module814 and execution module 816.

Updating module 814 is configured to update a state of a task flow. Insome examples, each task flow has a respective state. Generally, a taskflow state includes a data structure representing the task flow'scurrent interpretation of the natural language interaction. For example,a set of one or more parameters having a respective set of one or morevalues define a state of the task flow. For example, the performtranslation task flow has the parameters {input language}, {input text},{target language}, and {translation result}. Values for each of theseparameters respectively indicate the language of the text to translate,the text to translate, the language to translate the text, and thetranslation result. Accordingly, in some examples, updating a state of atask flow includes determining a respective value for a parameter of thetask flow.

In some examples, updating module 814 is configured to update a state ofa task flow based on the corresponding representation of user intent.For example, for the user intent to translate “hello” (based on speechportion 910), updating module 814 updates state of the first performtranslation task flow to {{input text=“hello”}, {targetlanguage=[null]}, {translation result=[null]}}, where [null] indicatesthat the value is not determined or specified. For the user intent totranslate “hello” to Spanish (based on speech portion 912), updatingmodule 814 updates the state of the second perform translation task flowto{{input text=“hello”}, {target language=Spanish}, {translationresult=[null]}}. In some examples, for the user intent to translate“yellow” (based on speech portion 910), updating module 814 updates thestate of the third perform translation task flow to {{inputtext=“yellow”}, {target language=[null]}, {translation result=[null]}}.In some examples, for the user intent to translate “yellow” to Spanish(based on speech portion 912), updating module 814 updates the state ofthe fourth perform translation task flow to {{input text=“yellow”},{target language=Spanish}, {translation result=[null]}}.

In some examples, execution module 816 is configured to execute a taskflow based on the task flow's current (updated) state. For example,execution module 816 is configured to determine or perform the actionsof the task flow according to the determined value(s) of the task flow.As a specific example, after a first value is determined, the task flowexecutes to determine or perform a next action to determine a subsequentvalue. In some examples, when all necessary values are determined (or anecessary value cannot be determined), execution of the task flowdetermines a next action to provide a result (or ask a user for furtherinformation/clarification). For example, based on the updated state{{input text=“hello”}, {target language=[null]}, {translationresult=[null]}} of the first perform translation task flow, executionmodule 816 executes the task flow to determine the next action to output“to what language?” That is, based on the current interpretation ofspeech portion 910 “translate hello,” the first task flow determinesthat the target translation language (a necessary parameter) isunspecified, and thus determines the action to ask for the translationlanguage. (Recall that the first task flow is based on speech portion910 “translate hello,” not speech portion 912 “translate hello toSpanish”). In some examples, based on the updated state {{inputtext=“yellow”}, {target language=[null]}, {translation result=[null]}}of the third perform translation task flow, execution module 816similarly executes the task flow to determine the next action to output“to what language?”

As previously mentioned, because module 812 executes task flowsspeculatively, it may be desirable to limit the types of actionsperformed by executing the task flows. In particular, because the taskflows might incompletely (and/or incorrectly) represent the actual userrequest, it may be undesirable to provide user-perceptible outputresulting from executing the task flows (as discussed). For example, itmay be undesirable to ask a user “to what language?” when the user says“translate hello to Spanish.” Performing other types of (potentiallywrong) actions based on executing the task flows may also beundesirable. For example, it may be undesirable to perform actionsrequiring high device processing power when the corresponding task flowmight be incorrect.

Accordingly, in some examples, each action determined (proposed) byexecution module 816 is considered a candidate action. Execution module816 determines whether each proposed candidate action is of apredetermined type. Execution module 816 then selectively determineswhether to perform the candidate action based on whether the candidateaction is of the predetermined type. Predetermined types of actionsdescribe actions undesirable for execution module 816 to perform, e.g.,undesirable to perform before determining an end of the natural languageinput (e.g., end time 908). Accordingly, in accordance with determiningthat a candidate action is of a predetermined type, execution module 816forgoes performing the candidate action, and instead outputs anexecutable object representing the candidate action. In accordance withdetermining that a candidate action is not of the predetermined type,execution module 816 performs the candidate action, e.g., as part ofexecuting the task flow.

An example predetermined type of action corresponds to an action that,when performed by a device, causes the device to provideuser-perceptible output (as previously discussed). Another examplepredetermined type of action corresponds to an action that, whenperformed by the device, causes the device to communicate with anexternal electronic device or communicate with an external service. Forexample, performing the action retrieves data from an external server orsends a request to a third party application installed on the device.Another example predetermined type of action corresponds to an actionthat, when performed by the device, has a computational cost exceeding athreshold cost. Another example predetermined type of action correspondsto an action that, when performed by the device, modifies a devicesetting or modifies (or generates) data viewable through a deviceapplication. For example, the actions of modifying a display brightness,modifying an audio volume, setting a timer, modifying or generating areminder item, modifying or generating a calendar item, modifying orgenerating a word processing document, and the like, correspond to apredetermined type of action. It will be appreciated that executionmodule 816 may consider whether a proposed candidate action is of someor all of these predetermined types when determining whether to performthe proposed candidate action.

In the example of FIG. 9, execution module 816 determines that thecandidate action to output “to what language?” proposed by the firstperform translation task flow is of the predetermined type. In someexamples, execution module 816 also determines that the (separate)candidate action to output “to what language?” proposed by the thirdperform translation task flow is of the predetermined type. Accordingly,execution module 816 forgoes performing either candidate action tooutput “to what language?” In this manner, execution module 816 maycheck each candidate action proposed by executing a task flow todetermine whether the candidate action is appropriate for performance bymodule 816.

Whether to perform candidate actions as part of executing a task flow(e.g., whether the candidate action is of a predetermined type) can alsodepend on other factors. For example, a candidate action is of thepredetermined type if a device's battery level is below a thresholdlevel, e.g., 20%. In another example, execution module 816 adjusts thethreshold computational cost controlling whether the action of thepredetermined type based on the device's battery level, e.g., a lowerthreshold cost for a lower battery level to prevent execution module 816from performing computationally expensive actions when the device haslow battery. In some examples, whether an action is of the predeterminedtype is based on a type of the device (e.g., the device's processingpower). For example, execution module 816 increases the thresholdcomputational cost for devices with relatively high processing power(e.g., a smartphone, laptop computer, desktop computer) and lowers thethreshold computational cost for devices with relatively low processingpower (e.g., a smart watch).

As an example of performing a candidate action (a preliminary action) aspart of executing a task flow, consider executing the second performtranslation task flow corresponding to the user intent of translating“hello” to Spanish. Based on the initial updated task flow state {{inputtext=“hello”}, {target language=Spanish}, {translation result=[null]}},execution module 816 executes the second perform translation task flowto obtain the proposed preliminary action to translate “hello” toSpanish. That is, given the current state, the perform translation taskflow determines the next step is to proceed with the requestedtranslation. Execution module 816 determines that the preliminary actionis not of the predetermined type, and thus performs the preliminaryaction to translate “hello” to Spanish. For example, execution module816 determines that performing the translation does not provideuser-perceptible output and/or determines, based on the device type(e.g., smartphone), that performing the translation does not have acomputational cost exceeding a threshold.

In some examples, updating module 814 updates a state of a task flowbased on performing the preliminary action to obtain a subsequentupdated task flow state. For example, updating module 814 updates thestate based on a value determined by performing the preliminary action.For example, performing the preliminary action to translate “hello” toSpanish returns the result “hola.” Updating module 814 thus updates thestate of the second perform translation task flow to the subsequentupdated task flow state {{input text=“hello”}, {targetlanguage=Spanish}, {translation result=“hola”}}.

In some examples, execution module 816 executes a task flow based on thesubsequent updated task flow state. For example, based on task flowstate {{input text=“hello”}, {target language=Spanish}, {translationresult=“hola”}}, execution module 816 executes the second performtranslation task flow to propose the candidate action to output “hola.”However, execution module 816 determines that the candidate action is ofthe predetermined type (e.g., performing the candidate action results inuser-perceptible output) and thus forgoes performing the candidateaction.

In some examples, execution module 816 executes the fourth performtranslation task flow (corresponding to the user intent of translating“yellow” to Spanish) in an analogous manner. For example, executionmodule 816, based on the initial updated task flow state {{inputtext=“yellow”}, {target language=Spanish}, {translation result=[null]}},proposes the candidate action to translate “yellow” to Spanish.Execution module 816 determines the candidate action is not of thepredetermined type, and thus translates “yellow” to Spanish to obtainthe result “amarillo.” Updating module 814 updates the task flow stateto the subsequent updated state {{input text=“yellow”}, {targetlanguage=Spanish}, {translation result=“amarillo”}}. Based on thesubsequent updated task flow state, execution module 816 executes thefourth perform translation task flow to propose the candidate action tooutput “amarillo.” Execution module 816 determines that the candidateaction is of the predetermined type and thus forgoes performing thecandidate action. FIG. 10 (discussed further below) illustratesrepresentations of the four proposed candidate actions and correspondingcandidate text, according to various examples.

In accordance with determining that a proposed candidate action is ofthe predetermined type, speculative execution module 816determines/outputs an executable object representing the candidateaction. An executable object representing the candidate action includescomputer-executable instructions (e.g., code) that when executed, causesa device to perform the candidate action. Accordingly, in some examples,the executable object includes more than data for output, as such datamay not include any executable instructions. For example, the executableobject representing the candidate action to output “hola” includesexecutable instructions to output “hola,” not merely a datarepresentation of “hola,” e.g., synthesized speech for “hola.”

Accordingly, speculative execution module 812 outputs a plurality ofexecutable objects representing a respective plurality of candidateactions. In some examples, speculative execution module 812 stores theplurality of executable objects in memory, e.g., in the memory wherespeculative execution module 812 resides. Each candidate action isproposed by executing a respective task flow corresponding to arespective user intent representation. For example, executing the firstperform translation task flow outputs a first executable objectrepresenting the candidate action to output “to what language?”Executing the second perform translation task flow outputs a secondexecutable object representing the candidate action to output “hola.”Executing the third perform translation task flow outputs a thirdexecutable object representing the candidate action to output “to whatlanguage?” Executing the fourth perform translation task flow outputs afourth executable object representing the candidate action to output“amarillo.” Although these example executable objects each represent acandidate action to provide user-perceptible output, an executableobject may not represent a candidate action to provide user-perceptibleoutput. For example, an executable object can represent the candidateaction to translate “hello” (or “yellow”) to Spanish (but not to outputthe translation result).

In some examples, speculative execution module 812 annotates anexecutable object with a respective annotation. Each annotation mayindicate information relevant for selecting the candidate action toultimately perform, e.g., the executable object to execute. In thismanner, speculative execution module 812 may evaluate each task flow topromote selection of the correct candidate action.

In some examples, an annotation indicates that the correspondingcandidate action is of the predetermined type. For example, anannotation indicates that the corresponding candidate action, whenperformed by a device, causes the device to provide user-perceptibleoutput. For example, each of the four executable objects respectivelyrepresenting candidate actions to output “to what language?”, “hola,”“to what language?”, and “amarillo” have a respective annotationindicating the respective candidate action is of the predetermined type.As another example, an annotation indicates that performing thecorresponding candidate action has a computational cost exceeding athreshold. In some examples, an annotation indicates a computationalcost of performing the corresponding candidate action.

In some examples, an annotation indicates a speech recognitionconfidence score for the portion of the natural language inputcorresponding to the candidate action. For example, module 812 annotatesthe executable object representing the candidate action to output “hola”with a first speech recognition confidence score and annotates theexecutable object representing the candidate action to output “amarillo”with a second speech recognition confidence score. Recall that the firstspeech recognition confidence score (for “translate hello to Spanish”)may be higher than the second speech recognition confidence score (for“translate yellow to Spanish”). This may weigh in favor of selecting tooutput “hola” instead of “amarillo.”

In some examples, an annotation indicates a confidence score for therepresentation of user intent corresponding to the candidate action. Forexample, module 812 annotates the executable object representing thecandidate action to output “to what language?” (corresponding to theintent of translating “hello”) with a first confidence score. Module 812further annotates the executable object representing the candidateaction to output “to what language” (corresponding to the intent oftranslating “yellow”) with a second confidence score. The firstconfidence score may be higher than the second confidence score, meaningthat translating “hello” represents the user's intent better thantranslating “yellow.”

In some examples, an annotation indicates a success score associatedwith performing the candidate action. In some examples, the successscore is binary, e.g., 0 for a failed action and 1 for a successfulaction. In other examples, the success score falls within apredetermined range, e.g., 0 to 1, with higher scores for successfulactions. In some examples, a failed action includes an action indicatingan error or indicating an inability to complete or understand a userrequest. For example, candidate actions to output “sorry, I don'tunderstand,” “I can't do that,” “can you repeat that?”, and “I couldn'tfind what you're looking for” are failed actions. In some examples,actions other than failed action are considered successful actions.

In some examples, an annotation indicates a length of the naturallanguage input portion corresponding to the candidate action. Forexample, the annotation indicates a character or word count of thenatural language portion (e.g., speech portions 910 and 912) orindicates a temporal length of the natural language portion. In someexamples, an annotation indicates whether the natural language portionincludes a completed user request (e.g., whether module 802 determinesthe portion includes all natural language input received between a starttime and an end time). For example, module 812 annotates the executableobject representing the candidate action to output “to what language?”to indicate a length of the natural language portion “translate hello”and to indicate that the portion does not include a completed userrequest. Module 812 annotates the executable object representing thecandidate action to output “hola” to indicate a greater length of thenatural language portion “translate hello to Spanish” and to indicatethat the portion includes a completed user request.

Task flow processing module 808 includes action selection module 818.Action selection module 818 is configured to receive executable objectsfrom speculative execution module 812 and select a candidate actionrepresented by a respective executable object.

In some examples, action selection module 818 selects a candidate actionbased on the respective annotations of the received executable objects.In some examples, module 818 implements a rule based or a probabilistic(e.g., machine learned) technique to select the candidate action. Forexample, module 818 collects the annotations of all received executableobjects and selects a candidate action by evaluating the annotations. Insome examples, the selection technique weighs in favor of selectingcandidate actions corresponding to higher speech recognition confidencescores and/or in favor of candidate actions corresponding to higher userintent representation confidence scores. In some examples, the selectiontechnique weighs against selecting candidate actions having highcomputational cost and/or against candidate actions having low successscores (e.g., failed actions). In some examples, the selection techniqueweighs in favor of selecting candidate actions corresponding to longernatural language portions (as longer portions may more completelyrepresent a user request) and/or corresponding to completed userrequests. It will be appreciated that the relative weight or importanceof each annotation (or whether an annotation is considered at all) whenselecting a candidate action can vary according to differentimplementations of module 818.

For example, module 818 selects between the (first) candidate action tooutput “to what language?”, the (second) candidate action to output “towhat language?”, the candidate action to output “hola,” and thecandidate action to output “amarillo.” For example, module 818 selectsthe candidate action to output “hola” because the annotations indicatethe speech recognition confidence score of “translate hello to Spanish”is the highest and/or because the annotations indicate that “translatehello to Spanish” includes a complete user request. In this manner,module 818 may accurately select a candidate action to perform, e.g., bydisfavoring, inter alia, failed candidate actions and candidate actionscorresponding to low speech recognition confidence scores.

In some examples, module 818 selects a candidate action in response tomodule 802 determining an end time of the natural language input, e.g.,end time 908. For example, speculative execution module 812 completesexecuting each task flow by the end time (meaning that action selectionmodule 818 has received an executable object for each task flow).Accordingly, at the end time, all candidate actions represented byrespective executable objects are available to select from.

In some examples, speculative execution module 812 does not completeexecuting each task flow by the end time. Accordingly, in some examples,module 818 waits for each task flow to complete executing (e.g., waitsto receive each executable object) and selects from the candidateactions upon receiving each executable object. In other examples, module818 selects from the available candidate actions at the end timeregardless of whether module 812 has completed executing every taskflow. For example, some executable objects/candidate actions are notavailable to select from when the selection is made. In other examples,module 818 selects from the candidate actions upon receiving apredetermined number of respective executable objects (e.g., 2), or uponreceiving at least one executable object with an annotation indicating acomplete user request.

Module 818 is further configured to execute the respective executableobject representing the selected candidate action to perform theselected candidate action. In some examples, performing the selectedcandidate action provides an output to a user. For example, module 818executes the executable object representing the candidate action tooutput “hola” to cause a device to output (e.g., speak and/or display)“hola.”

Operating a DA in this manner may reduce DA latency. For example, at (orshortly after) the end time of a natural language input, the DA maysimply execute a selected executable object to quickly provide aresponse to a user request. After the end time, the DA may not takeadditional time to process natural language input as described withrespect to modules 802, 804, 806, 810, and 812 to provide the response,as the DA already (at least partially) completed such processing priorto the end time.

Although the present example describes performing a candidate actioncorresponding to a complete user request (e.g., output “hola” responsiveto “translate hello to Spanish”), performing a candidate actioncorresponding to a complete user request is not always desirable. Thus,in some examples, module 818 selects (and performs) a candidate actionthat does not correspond to a complete user request. For example,suppose that the user actually said “translate hello to Finnish,” butdue to a speech recognition and/or natural language processing error,the DA determines the user intent to be translating hello to Spanish(albeit with a low confidence score). Module 812 thus outputs anexecutable object representing the candidate action to output “hola,”but annotates the executable object to indicate a low speech recognitionand/or user intent representation confidence score. Due to the lowconfidence score(s), module 818 instead selects the candidate action tooutput “to what language?” and performs the action. In this manner, theDA does not incorrectly output “hola” (which is not “hello” in Finnish)and instead asks the user to confirm the desired translation language.

In some examples, after providing an (initial) output of performing acandidate action, the DA receives a user input indicating a rejection ofthe initial output. Example user input indicating a rejection of theinitial output includes input canceling the initial output whileproviding the initial output (e.g., a touch or button input received ata device, a shaking motion of the device) and speech input, e.g.,“that's wrong,” “that's not what I meant,” “no,” and the like. In someexamples, the DA determines rejection of the initial output byprocessing the speech input as discussed with respect to modules 802,804, and 806, e.g., to determine a user intent of rejecting the initialoutput.

In some examples, in accordance with receiving the user input indicatingthe rejection, module 818 selects an alternative candidate action fromthe plurality of candidate actions. For example, module 818 ranks thecandidate actions based on the annotations discussed above and initiallyselects a top-ranked candidate action. If user input rejecting theinitial output of performing the top-ranked candidate action isreceived, module 818 selects a second-ranked (alternative) candidateaction. In some examples, module 818 further executes the respectivealternative executable object to perform the alternative candidateaction to provide another output.

For example, suppose the DA outputs “hola” (the top-ranked candidateaction) and the user responds “that's wrong” (because the user said“translate yellow to Spanish”). In response, module 818 selects thesecond-ranked candidate action to output “amarillo” and causes thedevice to output “amarillo.” In this manner, the DA is not required toreceive and process natural language input again to provide asatisfactory response, as the correct action is already available whenthe user rejects the initial output. For example, the user is notrequired to say “translate yellow to Spanish” again, as an availableexecutable object already represents the correct action to output“amarillo.”

In some examples, in accordance with receiving the user input indicatingthe rejection, module 818 provides a representation of one or morecandidate actions. For example, module 818 causes the device to provide(e.g., display and/or speak) text describing the candidate actions. Insome examples, selection module 818 further causes the device to providethe candidate text corresponding to each candidate action, e.g., inassociation with the text describing each candidate action.

For example, FIG. 10 illustrates device 200 providing representations ofcandidate actions and corresponding candidate text, according to variousexamples. Although FIG. 10 shows that device 200 provides textdescribing a rejected candidate action (e.g., to output “hola”), in someexamples, device 200 does not provide text describing a rejectedcandidate action (and the corresponding candidate text). In FIG. 10,each text describing a candidate action has a respective affordance,e.g., affordances 1002, 1004, 1006, and 1008. In some examples, device200 receives user input selecting an affordance, and in response,selection module 818 executes the executable object representing thecandidate action of the selected affordance. For example, a userselection of affordance 1006 causes device 200 to speak “to whatlanguage?”

In some examples, device 200 provides representations of candidateactions and associated candidate text without receiving user inputindicating a rejection. For example, device 200 provides the display ofFIG. 10 as an initial response to processing natural language input (asdiscussed with respect to FIG. 8). For example, responsive to receivingthe natural language input “translate hello to Spanish,” device 200provides the display of FIG. 10 instead of (or concurrently with)outputting “hola,” as discussed above.

Further details of speculative task flow execution are now discussedwith respect to FIG. 11. It will be appreciated that the techniquesdiscussed with respect to FIG. 11 may be combined with the techniquesdiscussed with respect to FIGS. 8-10 (and vice-versa) in mannerconsistent with the teachings herein.

FIG. 11 illustrates a timeline and waveform for received naturallanguage speech input 1100, according to various examples. Module 802determines start time 1102 and end time 1108 of speech input 1100, e.g.,“please call Ross.” Speech input 1100 includes speech portion 1110“please call” received from start time 1102 to first time 1104 andspeech portion 1112 “please call Ross” received from start time 1102 tosecond time 1106.

In some examples, DA module 800 processes speech input 1100 using inputprocessing module 802, STT processing module 804, and natural languageprocessing module 806 consistently with the techniques discussed above.For example, STT processing module 804 determines the candidate text“please call” for speech portion 1110 and the candidate text “pleasecall Ross” for speech portion 1112. Based on speech portion 1110 (andthe candidate text “please call”), natural language processing module806 determines a representation of a user intent to call. Based onspeech portion 1112 (and the candidate text “please call Ross”), naturallanguage processing module 806 determines a representation of a userintent to call an entity named Ross. Natural language processing module806 further provides each representation of user intent to task flowselection module 810.

Task flow selection module 810 selects a task flow corresponding to eachrepresentation of user intent. FIGS. 13A-C illustrate selection andexecution of task flows, according to various examples. For example, forthe representation of user intent to call, module 810 selects firstinstance of a task flow configured to perform phone calls (phone calltask flow 1300) in FIG. 13A. Similarly, for the representation of userintent to call an entity named Ross, module 810 selects second instanceof the phone call task flow 1302 in FIG. 13B. Module 810 provides eachinstance of the phone call task flow to speculative execution module812.

Updating module 814 updates a state of each received task flow, e.g.,based on the representation of user intent corresponding to each taskflow. As shown in FIGS. 13A-B, the parameters {entity to call} and{entity number}, for instance, define the phone call task flow state andrespectively indicate the identity of the entity to call and the phonenumber of the entity. For example, updating module 814 updates the stateof first instance of the phone call task flow 1300 (corresponding to theuser intent to call) to {{entity to call=[null]}, and {entitynumber=[null]}} in FIG. 13A. Updating module 814 updates the state ofsecond instance of the phone call task flow 1302 (corresponding to theuser intent to call an entity named Ross) to {{entity to call=Ross} and{entity number=[null]}} in FIG. 13B.

Execution module 816 executes each task flow based on the task flow'supdated state. For example, execution module 816, based on the state{{entity to call=[null]}, and {entity number=[null]}}, executes firstphone call task flow 1300 to propose the candidate action to output “whoyou do want to call?” Execution module 816 determines that the candidateaction is of the predetermined type, and thus does not perform thecandidate action. Instead, execution module 816 outputs executableobject 1304 representing the candidate action to output “who do you wantto call?”, as shown in FIG. 13A.

In some examples, execution module 816 implements stack based task flowexecution. That is, each task flow is an element of a task flow stackand execution module 816 pushes the initial task flow (e.g., phone calltask flow) as a bottom element of the task flow stack. In some examples,executing a task flow includes pushing another task flow (a sub-taskflow) onto the initial task flow. Implementing stack based task flowexecution can thus help organize the actions performed to satisfy a userintent by distributing the actions among different task flows. Forexample, executing a first task flow may determine that a second taskflow is better configured to perform actions to satisfy the user intent.For example, as discussed further below, each task flow includesinstructions/steps specifying a sub-task flow to push based on thecurrent state of the task flow, e.g., whether values for certainparameters defining the state are specified. As one example, the firsttask flow includes instructions specifying that when a value of thefirst task flow is unspecified, to push a task flow configured to obtainthe value (so the second task flow is better configured to satisfy theuser intent). For example, for some or all values of the first taskflow, the first task flow includes instructions for pushing one or moresub-task flows configured to obtain the value (e.g., a list of one ormore sub-task flows for the value) when executing the first task flowcannot determine the value. Accordingly, executing the first task flowincludes pushing the second (sub) task flow on top of the first taskflow and executing the second task flow. Executing the second task flowmay similarly determine that a third (sub) task flow is better tosatisfy the user intent. Thus, executing the second task flow includespushing the third task flow on top and executing the third task flow,and so on.

In some examples, execution module 816 coordinates with task flowselection module 810 to obtain a sub-task flow to be pushed. Forexample, each task flow includes programmed instructions for determining(e.g., based on the current task flow state) when to push sub-taskflow(s) onto the task flow stack and for obtaining the sub-task flow(s)from task flow selection module 810 (as each task flow included in taskflow selection module 810 may be pushed as a sub-task flow). As aspecific example, execution module 816 executes a task flow to determinethat a value for a parameter of the task flow state is unspecified. Inaccordance with such determination, execution module 816 instructsmodule 810 to push a sub-task flow configured to obtain the value. Insome examples, execution module 816 then executes the sub-task flow toobtain the value. The sub-task flow then provides the value back to thetask flow, e.g., based on instructions from the task flow to return thevalue.

In the example of FIGS. 11 and 13B-C, execution module 816 executesphone call task flow 1302 (corresponding to the user intent to call anentity named Ross) based on the updated state {{entity to call=Ross} and{entity number=[null]}}. Executing the phone call task flow determinesthat a value for the {entity number} parameter is unspecified. That is,phone call task flow 1302 interprets the user request as calling anentity named Ross, but is unable to disambiguate which Ross to call(e.g., a user's contact named Ross or a business named Ross).Accordingly, phone call task flow 1302 instructs to push two separatesub-task flows, one task flow configured to determine whether an entitycorresponds to a name in the user's contacts (contact name task flow1306) and another task flow configured to determine whether an entitycorresponds to a business name (business name task flow 1308). Forexample, phone call task flow 1302 includes instructions to push bothcontact name task flow 1306 and business name task flow 1308 when thevalue for {entity to call} is specified but the value for {entitynumber} is unspecified. In some examples, phone call task flow 1302further instructs contact name task flow 1306 and business name taskflow 1308 to return the {entity number}value, if possible. Accordingly,in some examples, execution module 816 obtains two separate task flowstacks, a first stack with phone call task flow 1302(a) as the bottomelement and contact name task flow 1306 as the top element and a secondstack with phone call task flow 1302(b) as the bottom element andbusiness name task flow 1308 as the top element, as shown in FIG. 13B.

Execution module 816 then executes (e.g., in parallel) contact name taskflow 1306 and business name task flow 1308 in a manner consistent thetechniques discussed herein. For example, the parameters {entity name},{is contact?}, and {entity number} define the state of contact name taskflow 1306 and respectively indicate the entity name, whether the entityis a contact, and the contact's phone number. Similarly, the parameters{entity name}, {is business?}, and {entity number} define the state ofbusiness name task flow 1308 and respectively indicate the entity name,whether the entity is a business, and the business' phone number.Updating module 814 updates the respective states of contact name andbusiness name task flows 1306 and 1308 (e.g., based on therepresentation of user intent to call Ross) and/or based on the state ofunderlying phone call task flow 1302. This determines the updatedcontact name task flow 1306 state {{entity name=Ross}, {iscontact=[null]}, and {entity number=[null]}} and the updated businessname task flow 1308 state {{entity name=Ross}, {is business=[null]}, and{entity number=[null]}}, as shown in FIG. 13B.

Continuing to FIG. 13C, execution module 816 then executes contact nametask flow 1306 and business name task flow 1308 based on theirrespective updated states. For example, executing contact name task flow1306 proposes (and performs) the candidate actions of searching a user'scontacts for Ross and determining the phone number of the contact namedRoss. Execution module 816 performs these proposed candidate actionsbecause neither are of the predetermined type. For example, neithersearching for a contact named Ross nor determining the phone number ofRoss cause a device to provide user-perceptible output. Accordingly,executing contact name task flow 1306 determines the task flow state{{entity name=Ross}, {is contact=yes}, and {entitynumber=123-456-7890}}, thus obtaining the desired value for the {entitynumber} parameter, as shown in FIG. 13C.

In some examples, execution of a sub-task flow outputs an executableobject. For example, executing business name task flow 1308 proposes(and performs) the action of searching for a business named Ross in alocal business directory. However, business name task flow 1308 isunable to find a business named Ross, resulting in the updated businessname task flow 1308 state {{entity name=Ross}, {is business=no}, and{entity number=[null]}}, as shown in FIG. 13C. Execution module 816executes business name task flow 1308 based on the updated state topropose the candidate action to output “sorry, I can't find a businessnamed Ross.” Because the candidate action is of the predetermined type,execution module 816 does not perform the candidate action, and insteadoutputs executable object 1310 representing the candidate action.

In some examples, execution module 816 pops a task flow from a task flowstack. For example, each task flow (e.g., sub-task flow) includesprogrammed instructions for determining when to pop the task flow. Forexample, the sub-task flow is popped when all possible actionsperformable by the sub-task flow to satisfy a user intent are performed(or proposed) or when executing the sub-task flow results in an error(e.g., proposing a candidate action indicating an error). As anotherexample, the sub-task flow is popped in accordance with obtaining avalue the sub-task flow was instructed to obtain.

For example in FIG. 13C, executing contact name task flow 1306 obtainsthe value “123-456-7890” for the {entity number} parameter and providesthe value to underlying phone call task flow 1302(a). Execution module816 then pops contact name task flow 1306 from the task flow stack. Insome examples, in accordance with business name task flow 1308 proposingthe action to output “sorry I can't find a business named Ross”(indicating an error), execution module 816 pops business name task flow1308 from the other task flow stack.

Executing contact name sub-task flow 1306 thus determines the underlyingphone call task flow 1302(a) state {{entity to call=Ross} and {entitynumber=123-456-7890}}. Execution module 816 executes phone call taskflow 1302(a) based on the state to propose the candidate action to callthe user's contact Ross at 123-456-7890. Execution module 816 determinesthat the candidate action is of the predetermined type, and thus forgoesperforming the candidate action. Instead, execution module 816 outputsexecutable object 1312 representing the candidate action to call theuser's contact Ross at 123-456-7890, as shown in FIG. 13C.

Implementing stack based task flow execution as described may enablescalable task flow deployment and accurate responses to a user request.For example, as each task flow of a task flow stack is configured toperform (or propose) a limited subset of actions, stack based executioncan coordinate and combine the different capabilities of each task flowin various manners, potentially allowing accurate responses to a widevariety of user requests. Further, the capabilities of each task flowmay be applicable to a plurality of other task flows, allowing greaterscalability and flexibility when using task flows. For example, thecontact name task flow may be pushed in any scenario where searching fora contact's name is desired (not just for disambiguating an entity forphone calls). For example, a task flow configured to send messages maypush the contact name task flow in an analogous manner, e.g., todetermine whether to send a message to a user's contact.

Processing speech input 1100 using speculative execution module 812 thusoutputs three executable objects (1304, 1310, and 1312) representingthree respective candidate actions. The three candidate actions are (1)to output “who do you want to call?” (based on speech portion 1110), (2)to output “sorry I can't find a business named Ross” (based on speechportion 1112) and (3) to call the user's contact Ross (based on speechportion 1112).

In some examples, module 812 further annotates each executable object,as described above. For example, module 812 annotates executable object1304 for “who do you want to call?” to indicate the corresponding speechportion 1110 does not include a completed user request and to indicatethe candidate action is successful. Module 812 annotates executableobject 1310 for “sorry I can't find a business named Ross” to indicatethe corresponding speech portion 1112 includes a completed user requestand to indicate the candidate action has failed. Module 812 annotatesexecutable object 1312 for calling the user's contact Ross to indicatethe corresponding speech portion 1112 includes a completed user requestand to indicate the candidate action is successful.

In some examples, action selection module 818 selects among thecandidate actions, e.g., in response to determining end time 1108. Forexample, module 818 selects among the candidate actions based on theannotations. For example, module 818 selects the candidate action tocall the user's contact Ross because the annotations indicate that thecandidate action is successful and/or the corresponding speech portionincludes a complete user request.

In some examples, module 818 further executes the executable objectrepresenting the selected candidate action to perform the action. Forexample, module 818 causes the device to call the user's contact Ross(e.g., executes executable object 1312) and display a user interfaceassociated with the phone call.

Although the examples of FIGS. 9, 11, and 13A-C relate to processingnatural language speech input, it should be appreciated that thetechniques discussed herein apply in an analogous manner to processingnatural language text input. For example, the processes described abovemay be performed using different portions of a text input received priorto the determined end time of the text input.

Accordingly, the techniques discussed with respect to FIGS. 8-11 and13A-C allow a DA to execute multiple task flows (or multiple task flowstacks) each corresponding to a different interpretation of a userrequest. Each task flow (or task flow stack) may be executed at leastpartially before determining an end time of the natural language input.The techniques discussed herein may thus enable quick and accurateperformance of actions to satisfy a user request. For example, executingthe multiple task flows may resolve ambiguity in the natural languageinput and identify task flows corresponding to disfavored actions (e.g.,failed actions, actions corresponding to low speech recognitionconfidence, actions corresponding to incomplete user requests), therebypromoting performance of the correct action. For example, the DA may notoutput “sorry I can't find a business named Ross” responsive to “callRoss,” as the DA determines the action of calling the user's contactRoss is favored. Further, as the DA can identify favored and disfavoredactions before the natural language input is determined to end, the DAmay quickly perform the favored action upon determining the end time,e.g., compared to taking extra time to identify the favored action afterthe end time. For example, the executable object representing thefavored action may already be available for execution at the end time.

5. Process for Task Flow Execution

FIGS. 12A-E illustrate process 1200 for task flow execution, accordingto various examples. Process 1200 is performed, for example, using oneor more electronic devices implementing a digital assistant. In someexamples, process 1200 is performed using a client-server system (e.g.,system 100), and the blocks of process 1200 are divided up in any mannerbetween the server (e.g., DA server 106) and a client device. In otherexamples, the blocks of process 1200 are divided up between the serverand multiple client devices (e.g., a mobile phone and a smart watch).Thus, while portions of process 1200 are described herein as beingperformed by particular devices of a client-server system, it will beappreciated that process 1200 is not so limited. In other examples,process 1200 is performed using only a client device (e.g., user device104) or only multiple client devices. In process 1200, some blocks are,optionally, combined, the order of some blocks is, optionally, changed,and some blocks are, optionally, omitted. In some examples, additionalsteps may be performed in combination with the process 1200.

At block 1202, a natural language input having a start time is received(e.g., by input processing module 802) from a user of an electronicdevice. The natural language input includes a first portion receivedfrom the start time to a first time after the start time and a secondportion received from the start time to a second time after the firsttime.

At block 1204, in some examples, a first representation of user intentis determined (e.g., by natural language processing module 806) based onthe first portion of the natural language input. At block 1206, in someexamples, a second representation of user intent is determined based onthe second portion of the natural language input. At block 1208, in someexamples, a third representation of user intent is determined based onthe first portion of the natural language input. At block 1210, in someexamples, a fourth representation of user intent is determined based onthe second portion of the natural language input.

At block 1212, in some examples, a first task flow corresponding to thefirst representation of user intent is executed (e.g., by speculativeexecution module 812) at least partially between the first time and theend time. In some examples, executing the first task flow includesobtaining (e.g., by action selection module 818), based on the firstrepresentation of user intent, a first executable object representing afirst candidate action, of a plurality of candidate actions, inaccordance with a determination (e.g., by execution module 816) that thefirst candidate action is of a predetermined type, as shown in block1214. In some examples, the predetermined type of candidate actioncorresponds to an action that, when performed by the electronic device,causes the electronic device to provide user-perceptible output. In someexamples, the predetermined type of candidate action corresponds to asecond action that, when performed by the electronic device, causes theelectronic device to communicate with an external electronic device orcommunicate with an external service. In some examples, thepredetermined type of candidate action corresponds to a third actionthat, when performed by the electronic device, has a computational costexceeding a threshold cost.

In some examples, the first task flow is a first element of a task flowstack. In some examples, executing the first task flow includesdetermining (e.g., by execution module 816), based on the firstrepresentation of user intent, that a value for a parameter of the firsttask flow is unspecified, as shown in block 1216. In some examples,executing the first task flow includes, in accordance with determiningthat the value is unspecified, pushing (e.g., by execution module 816),as a second element of the task flow stack, a third task flow configuredto obtain the value, as shown in block 1218. In some examples, executingthe first task flow includes executing (e.g., by execution module 816)the third task flow to obtain the value, as shown in block 1220. In someexamples, executing the first task flow includes in accordance withobtaining the value, popping (e.g., by execution module 816) the thirdtask flow from the task flow stack, as shown in block 1222. In someexamples, executing the first task flow includes executing (e.g., byexecution module 816) the third task flow to obtain the first executableobject, as shown in block 1224.

In some examples, the first task flow has a first state defined by afirst set of one or more parameters. In some examples, executing thefirst task flow includes updating (e.g., by updating module 814) thefirst state, including determining, based on the first representation ofuser intent, a first set of one or more respective values for the firstset of one or more parameters, as shown in block 1226. In some examples,executing the first task flow includes executing (e.g., by executionmodule 816), based on the updated first state, the first task flow toobtain the first executable object, as shown in block 1228.

At block 1230, in some examples, a second task flow corresponding to thesecond representation of user intent is executed (e.g., by speculativeexecution module 812) at least partially between the second time and theend time. In some examples, executing the second task flow includesobtaining (e.g., by action selection module 818), based on the secondrepresentation of user intent, a second executable object representing asecond candidate action, of the plurality of candidate actions, inaccordance with a determination (e.g., by execution module 816) that thesecond candidate action is of the predetermined type, as shown in block1232. In some examples, the first executable object is not executedprior to determining the end time and the second executable object isnot executed prior to determining the end time. In some examples,executing the first task flow and the second task flow is performedwithout providing, by the electronic device, user-perceptible output.

In some examples, the second task flow has a second state defined by asecond set of one or more parameters. In some examples, executing thesecond task flow includes updating (e.g., by updating module 814) thesecond state to obtain an initial updated second state, includingdetermining, based on the second representation of user intent, a secondset of one or more respective values for the second set of one or moreparameters, as shown in block 1234. In some examples, executing thesecond task flow includes executing (e.g., by execution module 816),based on the initial updated second state, the second task flow toobtain a preliminary action, as shown in block 1236. At block 1238, insome examples, it is determined (e.g., by execution module 816) whetherto perform the preliminary action based on a type of the electronicdevice. In some examples, executing the second task flow includesperforming (e.g., by execution module 816) the preliminary action inaccordance with a determination that the preliminary action is not ofthe predetermined type, as shown in block 1240. In some examples,executing the second task flow includes updating (e.g., by updatingmodule 814), based on performing the preliminary action, the secondstate to obtain a subsequent updated second state, as shown in block1242. In some examples, executing the second task flow includesexecuting (e.g., by execution module 816), based on the subsequentupdated second state, the second task flow to obtain the secondexecutable object, as shown in block 1244.

In some examples, at block 1246, a fourth task flow corresponding to thethird representation of user intent is executed (e.g., by speculativeexecution module 812) at least partially between the first time and theend time. In some examples, executing the fourth task flow includesobtaining (e.g., by action selection module 818), based on the thirdrepresentation of user intent, a fourth executable object representing afourth candidate action, of the plurality of candidate actions, inaccordance with a determination (e.g., by execution module 816) that thefourth candidate action is of the predetermined type, as shown in block1248.

In some examples, at block 1250, a fifth task flow corresponding to thefourth representation of user intent is executed (e.g., by speculativeexecution module 812) at least partially between the second time and theend time. In some examples, executing the fifth task flow includesobtaining (e.g., by action selection module 818), based on the fourthrepresentation of user intent, a fifth executable object representing afifth candidate action, of the plurality of candidate actions, inaccordance with a determination (e.g., by execution module 816) that thefifth candidate action is of the predetermined type, as shown in block1252.

At block 1254, in some examples, a plurality of respective executableobjects representing the plurality of candidate actions are stored(e.g., by action selection module 818) in memory.

At block 1256, in some examples, each of the plurality of executableobjects is annotated (e.g., by speculative execution module 812) with arespective annotation. In some examples, annotating each of theplurality of executable objects with a respective annotation includesannotating the first executable object with a first annotationannotating the second executable object with a second annotation. Insome examples, the first annotation indicates that the first candidateaction, when performed by the electronic device, causes the electronicdevice to provide user-perceptible output and the second annotationindicates that the second candidate action, when performed by theelectronic device, causes the electronic device to provideuser-perceptible output. In some examples, the first annotationindicates a first computational cost of performing the first candidateaction and the second annotation indicates a second computational costof performing the second candidate action. In some examples, the firstannotation indicates a first speech recognition confidence score for thefirst portion of the natural language input and the second annotationindicates a second speech recognition confidence score for the secondportion of the natural language input. In some examples, the firstannotation indicates a first confidence score for the firstrepresentation of user intent and the second annotation indicates asecond confidence score for the second representation of user intent. Insome examples, the first annotation indicates a first success scoreassociated with performing the first candidate action and the secondannotation indicates a second success score associated with performingthe second candidate action.

At block 1258, in some examples, an end time of the natural languageinput is determined (e.g., by input processing module 802).

At block 1260, in some examples, in response to determining the end timeof the natural language input, a candidate action is selected (e.g., byaction selection module 818) from the plurality of candidate actionseach represented by a respective executable object. In some examples,selecting the candidate action from the plurality of candidate actionsincludes selecting the candidate action based on the respectiveannotations, as shown in block 1262.

At block 1264, in some examples, the respective executable objectrepresenting the selected candidate action is executed (e.g., by actionselection module 818) to perform the selected candidate action, whereperforming the selected candidate action includes providing an output tothe user.

At block 1266, in some examples, after providing the output to the user,a user input indicating a rejection of the output is received. At block1268, in some examples, in accordance with receiving the user inputindicating the rejection, an alternative candidate action is selected(e.g., by action selection module 818) from the plurality of candidateactions. At block 1270, in some examples, in accordance with receivingthe user input indicating the rejection, the respective alternativeexecutable object representing the alternative candidate action isexecuted (e.g., by action selection module 818) to perform thealternative candidate action, where performing the alternative candidateaction includes providing a second output to the user.

The operations described above with reference to FIGS. 12A-E areoptionally implemented by components depicted in FIGS. 1-4, 6A-B, 7A-C,and 8. For example, the operations of process 1200 may be implemented byDA system 700 implementing DA module 800 (or a portion thereof). Itwould be clear to a person having ordinary skill in the art how otherprocesses are implemented based on the components depicted in FIGS. 1-4,6A-B, and 7A-C.

In accordance with some implementations, a computer-readable storagemedium (e.g., a non-transitory computer readable storage medium) isprovided, the computer-readable storage medium storing one or moreprograms for execution by one or more processors of an electronicdevice, the one or more programs including instructions for performingany of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises means forperforming any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises a processing unitconfigured to perform any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises one or moreprocessors and memory storing one or more programs for execution by theone or more processors, the one or more programs including instructionsfor performing any of the methods or processes described herein.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the techniques and their practical applications. Othersskilled in the art are thereby enabled to best utilize the techniquesand various embodiments with various modifications as are suited to theparticular use contemplated.

Although the disclosure and examples have been fully described withreference to the accompanying drawings, it is to be noted that variouschanges and modifications will become apparent to those skilled in theart. Such changes and modifications are to be understood as beingincluded within the scope of the disclosure and examples as defined bythe claims.

As described above, one aspect of the present technology is thegathering and use of data available from various sources to improvedigital assistant responses to user requests. The present disclosurecontemplates that in some instances, this gathered data may includepersonal information data that uniquely identifies or can be used tocontact or locate a specific person. Such personal information data caninclude demographic data, location-based data, telephone numbers, emailaddresses, twitter IDs, home addresses, data or records relating to auser's health or level of fitness (e.g., vital signs measurements,medication information, exercise information), date of birth, or anyother identifying or personal information.

The present disclosure recognizes that the use of such personalinformation data, in the present technology, can be used to the benefitof users. For example, the personal information data can be used by adigital assistant to respond to a user request. Further, other uses forpersonal information data that benefit the user are also contemplated bythe present disclosure. For instance, health and fitness data may beused to provide insights into a user's general wellness, or may be usedas positive feedback to individuals using technology to pursue wellnessgoals.

The present disclosure contemplates that the entities responsible forthe collection, analysis, disclosure, transfer, storage, or other use ofsuch personal information data will comply with well-established privacypolicies and/or privacy practices. In particular, such entities shouldimplement and consistently use privacy policies and practices that aregenerally recognized as meeting or exceeding industry or governmentalrequirements for maintaining personal information data private andsecure. Such policies should be easily accessible by users, and shouldbe updated as the collection and/or use of data changes. Personalinformation from users should be collected for legitimate and reasonableuses of the entity and not shared or sold outside of those legitimateuses. Further, such collection/sharing should occur after receiving theinformed consent of the users. Additionally, such entities shouldconsider taking any needed steps for safeguarding and securing access tosuch personal information data and ensuring that others with access tothe personal information data adhere to their privacy policies andprocedures. Further, such entities can subject themselves to evaluationby third parties to certify their adherence to widely accepted privacypolicies and practices. In addition, policies and practices should beadapted for the particular types of personal information data beingcollected and/or accessed and adapted to applicable laws and standards,including jurisdiction-specific considerations. For instance, in the US,collection of or access to certain health data may be governed byfederal and/or state laws, such as the Health Insurance Portability andAccountability Act (HIPAA); whereas health data in other countries maybe subject to other regulations and policies and should be handledaccordingly. Hence different privacy practices should be maintained fordifferent personal data types in each country.

Despite the foregoing, the present disclosure also contemplatesembodiments in which users selectively block the use of, or access to,personal information data. That is, the present disclosure contemplatesthat hardware and/or software elements can be provided to prevent orblock access to such personal information data. For example, in the caseof using personal information data to respond to user requests, thepresent technology can be configured to allow users to select to “optin” or “opt out” of participation in the collection of personalinformation data during registration for services or anytime thereafter.In another example, users can select not to provide personal informationdata to the digital assistant. In yet another example, users can selectto limit the length of time personal information data is maintained orentirely prohibit a digital assistant from accessing personalinformation data. In addition to providing “opt in” and “opt out”options, the present disclosure contemplates providing notificationsrelating to the access or use of personal information. For instance, auser may be notified upon downloading an app that their personalinformation data will be accessed and then reminded again just beforepersonal information data is accessed by the app.

Moreover, it is the intent of the present disclosure that personalinformation data should be managed and handled in a way to minimizerisks of unintentional or unauthorized access or use. Risk can beminimized by limiting the collection of data and deleting data once itis no longer needed. In addition, and when applicable, including incertain health related applications, data de-identification can be usedto protect a user's privacy. De-identification may be facilitated, whenappropriate, by removing specific identifiers (e.g., date of birth,etc.), controlling the amount or specificity of data stored (e.g.,collecting location data at a city level rather than at an addresslevel), controlling how data is stored (e.g., aggregating data acrossusers), and/or other methods.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data. That is, the various embodiments of thepresent technology are not rendered inoperable due to the lack of all ora portion of such personal information data. For example, a digitalassistant may respond to user requests based on non-personal informationdata or a bare minimum amount of personal information, such as thecontent being requested by the device associated with a user, othernon-personal information available to the digital assistant, or publiclyavailable information.

What is claimed is:
 1. A non-transitory computer-readable storage mediumstoring one or more programs, the one or more programs comprisinginstructions, which when executed by one or more processors of anelectronic device, cause the electronic device to: receive, from a userof the electronic device, a natural language input having a start time,the natural language input including: a first portion received from thestart time to a first time after the start time; and a second portionreceived from the start time to a second time after the first time;determine an end time of the natural language input; determine a firstrepresentation of user intent based on the first portion of the naturallanguage input; determine a second representation of user intent basedon the second portion of the natural language input; execute, at leastpartially between the first time and the end time, a first task flowcorresponding to the first representation of user intent, including:obtaining, based on the first representation of user intent, a firstexecutable object representing a first candidate action, of a pluralityof candidate actions, in accordance with a determination that the firstcandidate action is of a predetermined type; execute, at least partiallybetween the second time and the end time, a second task flowcorresponding to the second representation of user intent, including:obtaining, based on the second representation of user intent, a secondexecutable object representing a second candidate action, of theplurality of candidate actions, in accordance with a determination thatthe second candidate action is of the predetermined type; in response todetermining the end time of the natural language input, select acandidate action from the plurality of candidate actions eachrepresented by a respective executable object; and execute therespective executable object representing the selected candidate actionto perform the selected candidate action, wherein performing theselected candidate action includes providing an output to the user. 2.The non-transitory computer-readable storage medium of claim 1, wherein:the first executable object is not executed prior to determining the endtime; and the second executable object is not executed prior todetermining the end time.
 3. The non-transitory computer-readablestorage medium of claim 1, wherein: executing the first task flow andthe second task flow is performed without providing, by the electronicdevice, user-perceptible output.
 4. The non-transitory computer-readablestorage medium of claim 1, wherein the one or more programs furthercomprise instructions, which when executed by the one or moreprocessors, cause the electronic device to: store, in the memory, theplurality of respective executable objects representing the plurality ofcandidate actions.
 5. The non-transitory computer-readable storagemedium of claim 1, wherein the predetermined type of candidate actioncorresponds to an action that, when performed by the electronic device,causes the electronic device to provide user-perceptible output.
 6. Thenon-transitory computer-readable storage medium of claim 1, wherein thepredetermined type of candidate action corresponds to a second actionthat, when performed by the electronic device, causes the electronicdevice to communicate with an external electronic device or communicatewith an external service.
 7. The non-transitory computer-readablestorage medium of claim 1, wherein the predetermined type of candidateaction corresponds to a third action that, when performed by theelectronic device, has a computational cost exceeding a threshold cost.8. The non-transitory computer-readable storage medium of claim 1,wherein: the first task flow is a first element of a task flow stack;and executing the first task flow includes: determining, based on thefirst representation of user intent, that a value for a parameter of thefirst task flow is unspecified; and in accordance with determining thatthe value is unspecified, pushing, as a second element of the task flowstack, a third task flow configured to obtain the value.
 9. Thenon-transitory computer-readable storage medium of claim 8, whereinexecuting the first task flow includes: executing the third task flow toobtain the value; and in accordance with obtaining the value, poppingthe third task flow from the task flow stack.
 10. The non-transitorycomputer-readable storage medium of claim 8, wherein executing the firsttask flow includes: executing the third task flow to obtain the firstexecutable object.
 11. The non-transitory computer-readable storagemedium of claim 1, wherein the first task flow has a first state definedby a first set of one or more parameters, and wherein executing thefirst task flow includes: updating the first state, including:determining, based on the first representation of user intent, a firstset of one or more respective values for the first set of one or moreparameters; and executing, based on the updated first state, the firsttask flow to obtain the first executable object.
 12. The non-transitorycomputer-readable storage medium of claim 1, wherein the one or moreprograms further comprise instructions, which when executed by the oneor more processors, cause the electronic device to: annotate each of theplurality of executable objects with a respective annotation including:annotating the first executable object with a first annotation; andannotating the second executable object with a second annotation,wherein selecting the candidate action from the plurality of candidateactions includes selecting the candidate action based on the respectiveannotations.
 13. The non-transitory computer-readable storage medium ofclaim 12, wherein: the first annotation indicates that the firstcandidate action, when performed by the electronic device, causes theelectronic device to provide user-perceptible output; and the secondannotation indicates that the second candidate action, when performed bythe electronic device, causes the electronic device to provideuser-perceptible output.
 14. The non-transitory computer-readablestorage medium of claim 12, wherein: the first annotation indicates afirst computational cost of performing the first candidate action; andthe second annotation indicates a second computational cost ofperforming the second candidate action.
 15. The non-transitorycomputer-readable storage medium of claim 12, wherein: the firstannotation indicates a first speech recognition confidence score for thefirst portion of the natural language input; and the second annotationindicates a second speech recognition confidence score for the secondportion of the natural language input.
 16. The non-transitorycomputer-readable storage medium of claim 12, wherein: the firstannotation indicates a first confidence score for the firstrepresentation of user intent; and the second annotation indicates asecond confidence score for the second representation of user intent.17. The non-transitory computer-readable storage medium of claim 12,wherein: the first annotation indicates a first success score associatedwith performing the first candidate action; and the second annotationindicates a second success score associated with performing the secondcandidate action.
 18. The non-transitory computer-readable storagemedium of claim 1, wherein the one or more programs further compriseinstructions, which when executed by the one or more processors, causethe electronic device to: determine a third representation of userintent based on the first portion of the natural language input;execute, at least partially between the first time and the end time, afourth task flow corresponding to the third representation of userintent, including: obtaining, based on the third representation of userintent, a fourth executable object representing a fourth candidateaction, of the plurality of candidate actions, in accordance with adetermination that the fourth candidate action is of the predeterminedtype; determine a fourth representation of user intent based on thesecond portion of the natural language input; and execute, at leastpartially between the second time and the end time, a fifth task flowcorresponding to the fourth representation of user intent, including:obtaining, based on the fourth representation of user intent, a fifthexecutable object representing a fifth candidate action, of theplurality of candidate actions, in accordance with a determination thatthe fifth candidate action is of the predetermined type.
 19. Thenon-transitory computer-readable storage medium of claim 1, wherein thesecond task flow has a second state defined by a second set of one ormore parameters, and wherein executing the second task flow includes:updating the second state to obtain an initial updated second state,including: determining, based on the second representation of userintent, a second set of one or more respective values for the second setof one or more parameters; executing, based on the initial updatedsecond state, the second task flow to obtain a preliminary action;performing the preliminary action in accordance with a determinationthat the preliminary action is not of the predetermined type; updating,based on performing the preliminary action, the second state to obtain asubsequent updated second state; and executing, based on the subsequentupdated second state, the second task flow to obtain the secondexecutable object.
 20. The non-transitory computer-readable storagemedium of claim 19, wherein the one or more programs further compriseinstructions, which when executed by the one or more processors, causethe electronic device to: determine whether to perform the preliminaryaction based on a type of the electronic device.
 21. The non-transitorycomputer-readable storage medium of claim 1, wherein the one or moreprograms further comprise instructions, which when executed by the oneor more processors, cause the electronic device to: after providing theoutput to the user: receive a user input indicating a rejection of theoutput; in accordance with receiving the user input indicating therejection: select an alternative candidate action from the plurality ofcandidate actions; and execute the respective alternative executableobject representing the alternative candidate action to perform thealternative candidate action, wherein performing the alternativecandidate action includes providing a second output to the user.
 22. Anelectronic device, comprising: one or more processors; a memory; and oneor more programs, wherein the one or more programs are stored in thememory and configured to be executed by the one or more processors, theone or more programs including instructions for: receiving, from a userof the electronic device, a natural language input having a start time,the natural language input including: a first portion received from thestart time to a first time after the start time; and a second portionreceived from the start time to a second time after the first time;determining an end time of the natural language input; determining afirst representation of user intent based on the first portion of thenatural language input; determining a second representation of userintent based on the second portion of the natural language input;executing, at least partially between the first time and the end time, afirst task flow corresponding to the first representation of userintent, including: obtaining, based on the first representation of userintent, a first executable object representing a first candidate action,of a plurality of candidate actions, in accordance with a determinationthat the first candidate action is of a predetermined type; executing,at least partially between the second time and the end time, a secondtask flow corresponding to the second representation of user intent,including: obtaining, based on the second representation of user intent,a second executable object representing a second candidate action, ofthe plurality of candidate actions, in accordance with a determinationthat the second candidate action is of the predetermined type; inresponse to determining the end time of the natural language input,selecting a candidate action from the plurality of candidate actionseach represented by a respective executable object; and executing therespective executable object representing the selected candidate actionto perform the selected candidate action, wherein performing theselected candidate action includes providing an output to the user. 23.A method, comprising: at an electronic device with one or moreprocessors and memory: receiving, from a user of the electronic device,a natural language input having a start time, the natural language inputincluding: a first portion received from the start time to a first timeafter the start time; and a second portion received from the start timeto a second time after the first time; determining an end time of thenatural language input; determining a first representation of userintent based on the first portion of the natural language input;determining a second representation of user intent based on the secondportion of the natural language input; executing, at least partiallybetween the first time and the end time, a first task flow correspondingto the first representation of user intent, including: obtaining, basedon the first representation of user intent, a first executable objectrepresenting a first candidate action, of a plurality of candidateactions, in accordance with a determination that the first candidateaction is of a predetermined type; executing, at least partially betweenthe second time and the end time, a second task flow corresponding tothe second representation of user intent, including: obtaining, based onthe second representation of user intent, a second executable objectrepresenting a second candidate action, of the plurality of candidateactions, in accordance with a determination that the second candidateaction is of the predetermined type; in response to determining the endtime of the natural language input, selecting a candidate action fromthe plurality of candidate actions each represented by a respectiveexecutable object; and executing the respective executable objectrepresenting the selected candidate action to perform the selectedcandidate action, wherein performing the selected candidate actionincludes providing an output to the user.